{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../assets/logo.png\" width=\"50\" align=\"left\"> \n",
    "\n",
    "# Doppler\n",
    "\n",
    "***\n",
    "\n",
    "#### Prerequesites\n",
    "- Sampling Data\n",
    "- Range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import numpy as np                  # Scientific Computing Library\n",
    "import matplotlib.pyplot as plt     # Basic Visualization Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro - Back to Our Previous Example\n",
    "\n",
    "<img src=\"../assets/speedometer.png\" width=\"400\">\n",
    "\n",
    "Think back to our movie example, we went through some steps to extracting range information from our radar data. This is good for reconstructing the dramatic movie scene, but there are still a lot of things missing. One piece of key information that doesn't necessarily show up on the screen is still left to be discovered. That, of course, is speed. By using radar, we should be able to find out how fast an object is approaching, or fleeting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What the Radar Sees\n",
    "\n",
    "Last time, we used the way the ADC samples were captured to find range. We said that since they were captured equispaced apart in time, they formed a time log of sorts we could access. Conceptually, let's think about what would happen if we used that same technique for capturing chirps as well as samples. \n",
    "\n",
    "Some object in front of the radar will reflect portions of the first chirp. Now the second, third, and so on chirps hit this object and reflect. For simplicity, let's assume that the object is stayed within a single range bin for the duration of the entire frame and is moving at a constant velocity. There should be some set of samples across the separate chirps that hold this valuable information. Think about what each of them look like relative to the previous and next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Doppler Effect\n",
    "\n",
    "The doppler effect is what happens when a wave hits a moving object. The intuition behind it is simple, if you throw a bouncy ball at something moving it will either return faster or slower than normal. For waves, what is affected is the frequency of the wave after it bounces off of the object since the peaks in the wave either spread out or get closer together. This means there is some relation between the frequency of the wave and the velocity of the object it hits (additionally the source of the wave if it isn't already stationary). The elementary equation for this is...\n",
    "\n",
    "$f' = \\frac{v+v_0}{v-v_s}f$\n",
    "\n",
    "However, this equation works with sound waves (which do not obey the equation $c=\\lambda f$ that the EM waves radar generates). We need a new method for finding this change in velocity with waves that are moving at the speed of light."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discovering Meaning in the Chirps\n",
    "\n",
    "In addition to multiple ADC samples, we have multiple chirps. For an object at range $x$ from the radar, when we receive the respective ADC sample, the product will be a complex number with some phase. If the object is moving away from the radar, the respective ADC sample of the second chirp will come in at a very slightly delayed time. This is because the object also moved slightly away in that miniscule amount of time. Althought this movement is miniscule, the change in phase of the wave can be clearly seen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processing Doppler Information\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1 - Range FFT\n",
    "\n",
    "Let's start with something that we've already done, but expand it slightly. We know from the last reading that we can do a FFT across the data samples we receive to get range information. However, the data I'm providing this time is from a whole **frame** instead of a single **chirp**. Adjust your method to account for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in frame data\n",
    "frame = np.load('../assets/simple_frame_1.npy')\n",
    "\n",
    "# Manually cast to signed ints\n",
    "frame.real = frame.real.astype(np.int16)\n",
    "frame.imag = frame.imag.astype(np.int16)\n",
    "\n",
    "# Meta data about the data\n",
    "num_chirps = 128 # Number of chirps in the frame\n",
    "num_samples = 128 # Number of ADC samples per chirp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n    Task: Perform the range FFt on the entire frame\\n    Output: range_plot [chirps, range_bins]\\n    \\n    Details: This should be very similar to last time, although the input is now of shape [chirps, adc_samples]\\n'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#################### TODO ####################\n",
    "\"\"\"\n",
    "    Task: Perform the range FFt on the entire frame\n",
    "    Output: range_plot [chirps, range_bins]\n",
    "    \n",
    "    Details: This should be very similar to last time, although the input is now of shape [chirps, adc_samples]\n",
    "\"\"\"\n",
    "\n",
    "# Range FFT\n",
    "\n",
    "##############################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" REMOVE \"\"\"\n",
    "range_plot = np.fft.fft(frame, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize Results\n",
    "plt.imshow(np.abs(range_plot).T)\n",
    "plt.ylabel('Range Bins')\n",
    "plt.title('Interpreting a Single Frame - Range')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 - Doppler FFT\n",
    "\n",
    "Looking into the produced plot, it's starting to look like the radar definitely sees something at various ranges. Notably, we can see peak lines at range bins ~40 and ~115. Still, it's hard to tell exactly what. We should now try and also obtain velocity information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SWlUyuRz4IoOV6i2AFWYWy8UsBqaWe6Kk0yXNlzS/h3X5R+qcS6XpJRNJxwDLzex+STOKi8tsWjYfmtksYBbABE22xIrKB03bBlJNue2qlYrKtdOkWZaFaiUU68f6BzuabfScuM6KozL29Q28EYrtJFrfy+rpofSx9oi9ARj/6HL6V60KGxbHLOlL9GIrto+8GUsv1UolZdtEGvjeq9bekaYUUqttrdp25UqftZ6bRrXPYb37LBfbEBq0W1HNORj4kKSjgdHABEJJZaKk7lg6mQa82ILYnHMNanoyMbPzgPMAYsnkC2Z2sqQbgeMJV3RmArc0KaChb5e2LSaLNpt6VOrfHpcPFACqtN1sEF28StO19k3edvUuAPR3hxJD/9LlWF/cYSzB2AYlk7AnxXYXrL++156mr37F52bwLd7o+5n1+5vl/mq1xdSpnfqZnAPcIOlC4K/ANS2OpzNUK7pX225geddg/5KRI8PvMaMpPLUkPC72Gxk5YjB5lLtUHffft8VmYdmzVWLwPiPDUkuTiZndDdwdHz8N7N/KeJxzjWunkonLQ5pv+arb9A/UMIqNp+rro2vs2A13sa7MlTXb+B6dwrIVAPQ2u8rncuf35jjnMuElE1dducvX/YaVa2Ttj4/jvTyDbSeJBt6RI3IM1rWSJ5Oh6OQGw+RrG3h9xWqLoL+kL0iyb0hp9SbR+7d/2UvVj+WGLa/mOOcy4SWToejkb9JaPThLLwMnDfRWLTNIUl+Z/iKdfB43IV4ycc5lwksmbkPV7jMqNzZKvKPY+vroigMmMSI2ssZ2FevtHbhzWOPiJeX1iYGlS0s3acfjcG2lZslE0lmSJii4RtIDkj7QjOBcmyg2wvb3QX8f1tuLmQ0OMQB0jRqFxowJP93dYSClri7o6kIjR6IR3WhEd7jS01WoPECTuspOr1FT2u1cbtJUc/7NzFYCHwC2Aj4GXJxrVM65YSdNNaeY7o8Gfmhmf5P8K6BjpblRTRqothT7mVhf32DVpUzP16Lit5f19m5clcnqZj7XEmlKJvdLuoOQTH4jaTPKjRTsnNukpSmZnArsDTxtZmskbUGo6jhXcgm59ndM1+ZhuMf+NWu8NNFhaiYTM+uXtAzYXZJf/XHOlVUzOUi6BPhn4FGgWKk14J4c43LDRbUBi5PbxHU+13DnSlPSOA7Y1cx89GYXmJXvG1Jt++LDdXEqpK7C4I2BriOkaYB9GvBbPZ1zVaUpmawBFkiaC4NzS5jZZ3KLyrU3KTELX3EYe6peEi7q2nIyAP2r12w8VMHA/rtS7cu1lzTJ5Nb445xzFaW5mjO7GYG44aXYHb44SBIS6g61YeupMkV0sWNbtfaSoXRecy1TMZlImmNmJ0h6CDaeEMvM9sw1MjfsDIy6Vqy+FBNG4mpO/8pVGz/Rb+rrCNVKJmfF38c0IxDn3PBWMZmY2ZL4e2CGE0lbAq+Y+VdIR6i3RJDoUzLYAJsYnqBSo2ni74HbupKDLvnHqSNUvDQs6UBJd0u6WdI+kh4GHgaWSTqyeSE654aDatWcK4Dzgc2B3wFHmdk8SbsB1wO3NyE+l6d6SwTJEkah2ABbMth0UrLtpFgiGTUq/F65sr5ju7ZXrdNat5ndYWY3AkvNbB6AmT021INKmijpJkmPSVoo6SBJkyXdKemJ+HvSUI/jchL7mQxUdYqKgygVByqy/sGpQeM6FbpQwUcL7UTV3tXkV83aknVDreR+G7jdzHYD9gIWAucCc81sOjA3/u2cGyaqVXP2krSS0LdxTHxM/Ht0oweUNAF4L3AKgJmtB9ZLOhaYETebTZiD+JxGj+OaLFllio/VHT5eyYm6+qZsGR4sXda00FxzVLuaU8jpmDsDLwE/lLQXcD/hMvQ2iStISyRtXe7Jkk4HTgcYzdhymzjnWqAVldduYF/gSjPbB1hNHVUaM5tlZvuZ2X4jGJVXjK6alA231tcXSiXJicuXv0Zh+Wt5ReZaqBXJZDGw2Mzui3/fREguyyRNAYi/l7cgNudcg5qeTMxsKfC8pF3josMIAy/dCsyMy2YCtzQ7NleH4pWaopRjjNuEcdiEcTkF5Vop1TCMknYAppvZbyWNIVw2LnOTRWqfBq6TNJIwXsrHCIltjqRTgeeADw9h/y5vxaEHijfllZs4q0x1yJ57sQnBuVZIM2zjxwkNnpOBtwLTgKsIJYqGmNkCYL8yqxrep3OutdJUc84ADgZWApjZE0DZKy2uwxU7ow1h2iRb34Ot93FgO1GaZLIu9gUBII5Q73dmOec2kCaZ/F7S+YSOa+8HbgR+mW9Yri0Vu8vXuz5RoumaMJ6uCePzi9G1TJpkci6hk9lDwH8HbgO+nGdQrk0lqzilV3OqKSYZM+jpDT8+w2zHSTUJF/D9+OOcc2WluZpTbtjG14H5wIVm9koegbk2ZCUDIjWgf51Pv9Sp0vQz+TVhJr+fxr9PjL9XAtcC/yX7sJxzw02aZHKwmR2c+PshSX8ws4MlfSSvwFwbM0vMeZNyJPliaaYv0cnNdZQ0DbDjJR1Q/EPS/kCxOb43l6icc8NOmpLJacAPJI0njGWyEjhN0jjgm3kG59pY2is5pQZmAJSXTjpMmqs5fwH2kLQ5IDNbkVg9J7fIXPsp1/CadoT74oBJI+KASdUm6nLDUpqrOaOAfwJ2BLqLUxWY2ddzjcw5N6ykqebcQrgUfD+JicvdJihZ+igtkaSttvQ3WD1ybS9NMplmZj5PjnOuqjRXc/4oaY/cI3HDR7LtZGBai5RDOfb2Dk527jpKmpLJIcApkhYRqjkCzCcu34SVGwgprULsn+IJpeOkSSZH5R6Fc27YS3Np+FmAOPVEw/PluA7QaGkkoWuz0N+xz+/R6Tg120wkfUjSE8Ai4PfAM4T7dZxzbkCaBthvAAcCfzeznQjjtP4h16hce6o1OFKaXaxZi60pnW3WdYI0yaQnDjPQJanLzO4C9s45LtehrKcX6/HG106UpgF2Rbwv5x7C9BTL8Rv8nHMl0pRMjgXWAJ8FbgeewscwcQ3S6FFotE/r2onSXM1ZHR/2A7MlFQgDJF2XZ2DOueGlYslE0gRJ50m6QtIHFJxJmIHvhOaF6NpSsTG2znl0NGokGjUyx8Bcq1Sr5vwY2JUwKv1pwB2EKTuPNbNjh3JQSZ+V9IikhyVdL2m0pJ0k3SfpCUk/i1OHOueGiWrVnJ3NbA8ASVcDLwPbD3GOYSRNBT4D7G5mayXNIVSbjgYuM7MbJF0FnApcOZRjuRw12IGt//UhfXxcG6tWMhmYw9HM+oBFQ00kCd2ESb26gbHAEuB9wE1x/WzguIyO5fJQ2uckZVVHhS5USNPu74abaiWTvSStjI9F+OdfyeCNfhMaOaCZvSDpUuA5YC2h+nQ/sMLMipecFwNTyz1f0umEidQZzdhGQnDO5aBiMjGzQh4HlDSJcLl5J2AFYbrRcjcTli0/m9ksYBbABE32QURbpbSak7K6ozFjwoM338whKNdKrShvHk6oMr1kZj3AzcA/ABNjtQdgGvBiC2JzzjWoFcnkOeBASWMVBpQ9DHgUuAs4Pm4zkzBcZHp1XqJ0LVLPHMVuWGl6MjGz+wgNrQ8QLjt3Eaot5wCfk/QksAVwTZ079qkTmqnB892zx8707LFzDgG5Vktzb07mzOwC4IKSxU8D+7cgHOdcBlqSTFwHKE4P2p9yetBoxN+eAsLk1a6z+AV/51wmvGTiGlMskdTZE9bW+0x+ncqTiWtMg93pvZ9J5/JqjnMuE14ycY1p9DK89zHpWF4ycc5lwksmrjENtpn4YNKdy0smzrlMeDJx9StOVN5Au4kPKN25vJrj6tdI42usFvXvHIepeeXVDANy7cBLJs65THjJxDWm3gbYuF3X488Cfm9OJ/KSiXMuE14ycY1psNNa/1rvRt+pPJm4xpSOapcyuXSNHwdA32t+w1+n8WqOcy4TXjJxQ1NvD9h163IKxLWal0ycc5nwkolrTIMNsNpsfHiwZk2Gwbh24MnENabRuYZfXZFDMK4deDXHOZcJL5m4+g1lsjMfHKljecnEOZcJTyaufsXhBxpohC1sszWFbbbOISjXarklE0k/kLRc0sOJZZMl3Snpifh7UlwuSd+R9KSkByXtm1dczrl85FkyuRY4smTZucBcM5sOzI1/AxwFTI8/pwNX5hiXy0KDE8X3v/Iq/T6WSUfKLZmY2T1A6afmWGB2fDwbOC6x/EcWzAMmSpqSV2yuTg0mjrK72nwC2nxCbvt3rdPsNpNtzGwJQPxdrDxPBZ5PbLc4LtuIpNMlzZc0vwfvmu1cu2iXS8PlvpbKtu6Z2SxgFsAETW5w8haXWnG814zYG6vLLPS3sSka7GiYVrNLJsuK1Zf4e3lcvhjYLrHdNODFJsfmnBuCZieTW4GZ8fFM4JbE8n+NV3UOBF4vVodcm2rw0nDXpIl0TZqYQ0Cupgbfs7Ryq+ZIuh6YAWwpaTFwAXAxMEfSqcBzwIfj5rcBRwNPAmuAj+UVl6uTWfnicYNF5r5tJ4UHi1/IIDjXTnJLJmZ2UoVVh5XZ1oAz8orFOZe/dmmAde2sXOmjweJy16LQFOaj03ce707vnMuEl0xcellcWtx2q/Dbe8F2HC+ZOOcy4SUTV10WndYSXeUX/dMWAGz/aLad4VzreTJx1SX/4YuPk/fRlEsIZS8lh0Lwx0+4HYDfXDgJKBkoyZPLsObVHOdcJrxk4uqX7MhWaX3p3xYuBt/26RkAFPofyCk41ypeMnHOZcJLJq6pRrwU5svxYaU7jyeTvOV82/dw07XmTcCTSSfyao5zLhNeMslbp5ZIGn1dycvLnXpuNlFeMnHOZcJLJq4xDbYFWcG/vzqVJxPXmAarKFrXM6Tnu/blXxPOuUx4ycQ1lY0a0eoQXE68ZOKcy4SXTFxjGu2Mt2Jl9rG4tuAlE+dcJrxk4pqqf6e3hAevvOpXdDqMJxPXVF0rwvSgfZ5IOo5Xc5xzmfCSiWsqGze61SG4nORWMpH0A0nLJT2cWPZ/JD0m6UFJv5A0MbHuPElPSnpc0hF5xeWcy0ee1ZxrgSNLlt0JvNPM9gT+DpwHIGl34ETgHfE535VUyDE21yJa/SZa/Warw3A5yC2ZmNk9wKsly+4ws9745zxgWnx8LHCDma0zs0WECcz3zys210JmfhWnQ7WyAfbfgF/Hx1OB5xPrFsdlG5F0uqT5kub3sC7nEJ1zabWkAVbSl4Be4LriojKblf36MrNZwCyACZrsX3HDjNb6F0CnanoykTQTOAY4zGygvLsY2C6x2TTgxWbH5pxrXFOrOZKOBM4BPmRmaxKrbgVOlDRK0k7AdODPzYzNNYeNHY2N9cvDnSi3komk64EZwJaSFgMXEK7ejALuVLhRbJ6ZfcLMHpE0B3iUUP05wyzO2uQ6ize+dqzckomZnVRm8TVVtr8IuCiveJxz+fIesK65Ro1sdQQuJ35vjnMuE14ycc3lbSYdy0smzrlMeMnENZV1+/dXp/Jk4ppq3dbjAPAx6juPf0045zIhG8YNYpJeAlYDL7c6FmBLPI4kj2NDwzmOHcxsq1obDetkAiBpvpnt53F4HB5Ha+Pwao5zLhOeTJxzmeiEZDKr1QFEHseGPI4NdXwcw77NxDnXHjqhZOKcawOeTJxzmRjWyUTSkXGenSclndukY24n6S5JCyU9IumsuHyypDslPRF/T2pSPAVJf5X0q/j3TpLui3H8TFLu9/xLmijppjgn0kJJB7XifEj6bHxPHpZ0vaTRzTofFeaJKnsOFHwnfm4flLRvznE0Zb6qYZtM4rw6/w4cBewOnBTn38lbL/B5M3s7cCBwRjzuucBcM5sOzI1/N8NZwMLE35cAl8U4XgNObUIM3wZuN7PdgL1iPE09H5KmAp8B9jOzdwIFwlxMzTof17LxPFGVzsFRhKFJpwOnA1fmHEdz5qsys2H5AxwE/Cbx93nAeS2I4xbg/cDjwJS4bArweBOOPY3wIX0f8CvCKP8vA93lzlFOMUwAFhEb8xPLm3o+GJwuZTLhnrNfAUc083wAOwIP1zoHwPeAk8ptl0ccJev+K3BdfLzB/wzwG+CgRo87bEsm1DHXTl4k7QjsA9wHbGNmSwDi762bENInEKAAAATDSURBVMLlwBeB/vj3FsAKG5zorBnnZGfgJeCHsbp1taRxNPl8mNkLwKXAc8AS4HXgfpp/PpIqnYNWfnYbmq8qjeGcTFLPtZPLwaXxwM+Bs81sZbOOmzj+McByM7s/ubjMpnmfk25gX+BKM9uHcK9Us6p4A2J7xLHATsBbgHGE6kSpdugL0ZLP7lDmq0pjOCeTls21I2kEIZFcZ2Y3x8XLJE2J66cAy3MO42DgQ5KeAW4gVHUuByZKKg4t0YxzshhYbGb3xb9vIiSXZp+Pw4FFZvaSmfUANwP/QPPPR1Klc9D0z25ivqqTLdZpso5jOCeTvwDTY2v9SEJD0q15H1Rhjo5rgIVm9q3EqluBmfHxTEJbSm7M7Dwzm2ZmOxJe++/M7GTgLuD4JsaxFHhe0q5x0WGEKUuaej4I1ZsDJY2N71ExjqaejxKVzsGtwL/GqzoHAq8Xq0N5aNp8VXk2iuX9AxxNaJ1+CvhSk455CKEo+CCwIP4cTWivmAs8EX9PbuJ5mAH8Kj7eOX4gngRuBEY14fh7A/PjOfkPYFIrzgfwNeAx4GHgx4Q5mppyPoDrCW01PYRv/FMrnQNC9eLf4+f2IcIVqDzjeJLQNlL8vF6V2P5LMY7HgaOGcmzvTu+cy8RwruY459qIJxPnXCY8mTjnMuHJxDmXCU8mzrlMeDLZBEnqk7Qg3mH7N0mfk5T5Z0HS3ZIaHrxY0lclvRBjfUzSlcU4JX1d0uHZReuGyifh2jStNbO9ASRtDfwU2By4oJVBSSqYWV/J4svM7NKYRO4BDgXuMrOvND9CV42XTDZxZraccBv8mbFH5mhJP5T0ULxx7x8BJJ0i6RZJt8exLy6Iy3eMpYbZcbyMmySNLT2OpA9I+pOkByTdGO9tQtIzkr4i6V7gw1VCHQmMJgwjgKRrJR2f2MfX4r4fkrRbXH5oLNUsiK9ls+zOnCvlycRhZk8TPgtbA2fEZXsAJwGzJY2Om+4PnEzo8frhRBVmV2CWhfEyVgKfSu5f0pbAl4HDzWxfQm/ZzyU2edPMDjGzG8qE91lJCwi9Ov9uZgsqvIyX476vBL4Ql30BOCOWwt4DrK19NlyjPJm4ouIdpIcQuqJjZo8BzwJvi+vuNLNXzGwt4Ua6Q+Ly583sD/HxTxLLiw4kDGD1h5gYZgI7JNb/rEpcl8VksDUwTtKJFbYr3nB5P2E8D4A/AN+S9Blgog0OReBy4MnEIWlnoI9wV2u529KLSu+9sBrLBw5BSER7x5/dzSw54tnqWjFauBP4duC9FTZZF3/3EdsCzexi4DRgDDCvWP1x+fBksomTtBVwFXCFhRu17iFUZZD0NmB7wk1gAO9XGNd0DHAc4ZsfYHtJB8XHJwH3lhxmHnCwpF3ifsfGfdcTpwhDCjxVx3PeamYPmdklhKqVJ5MceTLZNI0pXhoGfgvcQbjjFuC7QEHSQ4TqxylmVvzWv5dQBVoA/NzM5sflC4GZkh4kDJu4wZimZvYScApwfdxmHun/sYttJg8TShzfreN1nq0wuPTfCO0lv671BNc4v2vYpSLpFMKt8meWLN+RMPzBO1sQlmsjXjJxzmXCSybOuUx4ycQ5lwlPJs65THgycc5lwpOJcy4Tnkycc5n4//aEM7rNoSpqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take a sequential FFT across the chirps\n",
    "range_doppler = np.fft.fft(range_plot, axis=0)\n",
    "\n",
    "# FFT shift the values (explained later)\n",
    "range_doppler = np.fft.fftshift(range_doppler, axes=0)\n",
    "\n",
    "# Visualize the range-doppler plot\n",
    "# plt.imshow(np.log(np.abs(range_doppler).T))\n",
    "plt.imshow(np.abs(range_doppler).T)\n",
    "plt.xlabel('Doppler Bins')\n",
    "plt.ylabel('Range Bins')\n",
    "plt.title('Interpreting a Single Frame - Doppler')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(np.abs(range_doppler))\n",
    "plt.xlabel('Doppler Bins')\n",
    "plt.ylabel('Signal Strength')\n",
    "plt.title('Interpreting a Single Frame - Doppler')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting, there are definitely more things that we can tell from these plots just by decoding the velocity, but we need to know how to decipher them first. This is easy, and very similar to deciphering range from range bins. Additionally, we FFT shifted across the chirps to make it more intuitive to understand. That line in the middle at doppler bin 64 is called zero doppler, meaning everything along that line is static/not moving relative to the radar. This means everything to the left (bins<64) is negative doppler, or moving towards the radar and the opposite for the other half of the doppler bins.\n",
    "\n",
    "\n",
    "Some things that you may have observed:\n",
    "1. Much of the received signal translates to having zero doppler, which makes sense if you think about it because most of the objects around us (and the radar) are not moving and thus zero doppler relative to us.\n",
    "2. The plots show at range bin ~40, there is a grouping of peaks in intensity off to the left, meaning an object is most likely moving towards the radar.\n",
    "3. Also, at range bin ~115, we see there is a peak in the middle of the doppler bins, meaning there is probably a highly reflective static object in front of the radar. These described peaks are more clearly shown in the second plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note\n",
    "\n",
    "It's possible you noticed some things after finishing these two steps. \n",
    "1. Why did we take the range FFT/what if we don't need range?\n",
    "    - You really don't need to, but many tasks do require the range information that comes out of a radar. If yours doesn't need that information then you could probably get away with not taking it.\n",
    "2. Why did we have to take the range FFT before we did the doppler FFT?\n",
    "    - Again, no reason. Futhermore, for this case, the properies of the 2D FFT hold and we can actually do either direction. I can prove it right here..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max power difference:  5.64766185425834e-11\n"
     ]
    }
   ],
   "source": [
    "# Range FFT -> Doppler FFT\n",
    "range_bins = np.fft.fft(frame, axis=1)\n",
    "fft_2d = np.fft.fft(range_bins, axis=0)\n",
    "\n",
    "# Doppler FFT -> Range FFT\n",
    "doppler_bins = np.fft.fft(frame, axis=0)\n",
    "rfft_2d = np.fft.fft(doppler_bins, axis=1)\n",
    "\n",
    "print('Max power difference: ', np.abs(fft_2d - rfft_2d).max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which is pretty close to zero (not necessarily zero because of bit level rounding errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 - Unit Conversion\n",
    "\n",
    "All the units of the data we produced are of some type of \"bin\". Similarly to range resolution, we have a doppler resolution aka velocity resolution. This can be applied in a similar way, but first let's derive an equation. Again we start with some given equations...\n",
    "\n",
    "- $\\omega = \\frac{4\\pi vT_c}{\\lambda}$ - Rotational frequency of phasor due to object moving at $v$ velocity\n",
    "    - $v$ - Velocity\n",
    "    - $T_c$ - Sampling period\n",
    "    - $\\lambda$ - Wavelength\n",
    "- $\\Delta\\omega \\gt \\frac{2\\pi}{N}$ - Minimum change in rotation of phasor to be resolved by radar\n",
    "    - $N$ - Number of sample points\n",
    "\n",
    "Now its your turn to try and derive our velocity resolution equation. \n",
    "\n",
    "Solve this equation: $\\Delta v > ???$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range Resolution: 0.048828125 [meters/second]\n"
     ]
    }
   ],
   "source": [
    "# Data sampling configuration\n",
    "c = 3e8 # Speed of light (m/s)\n",
    "sample_rate = 2500 # Rate at which the radar samples from ADC (ksps - kilosamples per second)\n",
    "freq_slope = 60 # Frequency slope of the chirp (MHz/us)\n",
    "adc_samples = 128 # Number of samples from a single chirp\n",
    "\n",
    "start_freq = 77.4201 # Starting frequency of the chirp (GHz)\n",
    "idle_time = 30 # Time before starting next chirp (us)\n",
    "ramp_end_time = 62 # Time after sending each chirp (us)\n",
    "num_chirps = 128 # Number of chirps per frame\n",
    "num_tx = 2 # Number of transmitters\n",
    "\n",
    "# Range resolution\n",
    "range_res = (c * sample_rate * 1e3) / (2 * freq_slope * 1e12 * adc_samples)\n",
    "print(f'Range Resolution: {range_res} [meters/second]')\n",
    "\n",
    "# Apply the range resolution factor to the range indices\n",
    "ranges = np.arange(adc_samples) * range_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Velocity Resolution: 0.08226398402437117 [meters/second]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make sure your equation translates to the following\n",
    "velocity_res = c / (2 * start_freq * 1e9 * (idle_time + ramp_end_time) * 1e-6 * num_chirps * num_tx)\n",
    "print(f'Velocity Resolution: {velocity_res} [meters/second]')\n",
    "\n",
    "# Apply the velocity resolution factor to the doppler indicies\n",
    "velocities = np.arange(num_chirps) - (num_chirps // 2)\n",
    "velocities = velocities * velocity_res\n",
    "\n",
    "powers = np.abs(range_doppler)\n",
    "\n",
    "# Plot with units\n",
    "plt.imshow(powers.T, extent=[velocities.min(), velocities.max(), ranges.max(), ranges.min()])\n",
    "plt.xlabel('Velocity (meters per second)')\n",
    "plt.ylabel('Range (meters)')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(velocities, powers)\n",
    "plt.xlabel('Velocity (meters per second)')\n",
    "plt.ylabel('Reflected Power')\n",
    "plt.title('Interpreting a Single Frame - Doppler')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Analyze the Data\n",
    "\n",
    "***\n",
    "\n",
    "## Exercise\n",
    "\n",
    "I'm now going to give you some data that has *something* about it that neither range nor velocity themselves can solve. It's your job to try and figure out why this data is different. Doing this task and understanding why it's important will help you see how to make sense of radar data and start to see a bigger picture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of frame: (128, 8, 128)\n"
     ]
    }
   ],
   "source": [
    "# Read in data\n",
    "frame = np.load('../assets/doppler_example_1.npy')\n",
    "\n",
    "# Manually cast to signed ints\n",
    "frame.real = frame.real.astype(np.int16)\n",
    "frame.imag = frame.imag.astype(np.int16)\n",
    "\n",
    "print(f'Shape of frame: {frame.shape}')\n",
    "\n",
    "# Data configuration\n",
    "num_chirps = 128\n",
    "num_samples = 256\n",
    "\n",
    "num_rx = 4\n",
    "num_tx = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#################### TODO ####################\n",
    "\"\"\"\n",
    "    Task: Analyze the radar data (range)\n",
    "    Output: range_plot [chirps, range_bins]\n",
    "    \n",
    "    Details: First make some initial observations about the range data.\n",
    "    \n",
    "             Note that this frame actually has 3 different dimensions (axes) to it.\n",
    "             Axis 0 - Chirps\n",
    "             Axis 1 - Virtual Antennas ***ignore the deep meaning of this for now, just treat it as \n",
    "                                          8 copies of [128, 128] frames and work around it then accumulate them together***\n",
    "             Axis 2 - ADC Samples\n",
    "\"\"\"\n",
    "range_plot = None\n",
    "\n",
    "# Range FFT\n",
    "\n",
    "\n",
    "# Take magnitude and then accumulate (along the virtual antennas)\n",
    "\n",
    "\n",
    "##############################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" REMOVE \"\"\"\n",
    "range_plot = np.fft.fft(frame, axis=2)\n",
    "range_plot = np.abs(range_plot).sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the range plot for each chirp\n",
    "plt.plot(range_plot.T)\n",
    "plt.show()\n",
    "\n",
    "# Feel free to add any additional plots for futher analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#################### TODO ####################\n",
    "\"\"\"\n",
    "    Task: Analyze the radar data (range-doppler)\n",
    "    Output: range_doppler [doppler_bins, range_bins]\n",
    "    \n",
    "    Details: Figure out why this data is special by producing a range-doppler to work with. \n",
    "                (Hint: Look more closely at the peak at range bin ~60)\n",
    "    \n",
    "             Note that this frame actually has 3 different dimensions (axes) to it.\n",
    "             Axis 0 - Chirps\n",
    "             Axis 1 - Virtual Antennas ***ignore the deep meaning of this for now, just treat it as \n",
    "                                          8 copies of [128, 128] frames and work around it then accumulate them together***\n",
    "             Axis 2 - ADC Samples\n",
    "\"\"\"\n",
    "range_doppler = None\n",
    "\n",
    "# Range FFT\n",
    "\n",
    "\n",
    "# Doppler FFT and FFT shift\n",
    "\n",
    "\n",
    "# Take magnitude and then accumulate (along the virtual antennas)\n",
    "\n",
    "\n",
    "##############################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" REMOVE \"\"\"\n",
    "range_fft = np.fft.fft(frame, axis=2)\n",
    "range_doppler = np.fft.fft(range_fft, axis=0)\n",
    "range_doppler = np.fft.fftshift(range_doppler, axes=0)\n",
    "range_doppler = np.log(np.abs(range_doppler)).sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d18TexOaIG2a+6JDRCubq42adBRS6ufQ78R5n7YCsjeuI92l0deEPqT7c3Azqa6fotuP3hqK0cZe67rg5cvzF8zcRtic6Nv4uQ3kUlWa7H9dEPypMgWVqBdU34nf6SYG+Xl0LEty6ofKUtrfN0yyfzuczXw54PeTNtsk8h1Uo9+O4G2RZ1IxOTMHm/BQAkDWq4YIvnvw0PmgYFch0Mbb78SEsb84x1HE6+H8+o8Uh9qCP9LjtXm0LK5vF//tpjU4fjuIk/napm5I02zam5lw8Z9YOtvGZZhsE9bXF6nVycZw06CfxeO3laBpkyx79VlyM7VYc9+ilmY5xhFw1WaEb0eLxbbOGTPpgm1E24oaibsduZYutoPbPBUO56kJJH+xa6hvdymd4dARO4L9YxGNtPTegWMYFPbqmD6hu8t20sA1l0M2pWPS2KWa6sfXT0u4LHxxukkMobN6GkSqBKrOHiO5JPypQHN4RE4lQZuj24tyWN+Oc9Vs1+lGcZ1pR2dEcYaz3ZU/X0yE3ndI2xepWfC+fNWgO1II45rXHeV++7aKNkfezPG6BQR943RyLG3M0l+Ja52ZKi1OaAYXEsVVXT3QeS5+bsVpAWQbRDVNUqWSHce11OyOUt9bW4WuUN1sK+18CeLtE7pUKwH+N1RqTjWxkIw+ZvKksmRBCJyL/HWJJQA7gr4YQfuYuP4AMAd12jXyuVodqL/qz1a0lBvVdu111QyY5quhtmHbsdkYrJjQA9LrjF7dn6C0uEt+rXzo9GzRLpLgWTfTJklpd0Or56VKVtxfmyqUmeKaWAy2Ifpzbceur0UqxOMOoQKYWV8lAtfr02bI3V4dar742MyuuuTD18aoVUtyOGrs9N9FrG8x1GT2v8YZHphZ/ocV29FSNWl0yP36cs+q4RzeO4z0eNFaRWJic9+qGnnt/ZPErxn6kHTDoPPD6+jpHfS3OR6b331xMAP2I8QW1II4aBB639DllbINriPc9lBkKjZVQ+0s/oNTjDWrR9PsTj+PpvaCllM0aNJeim0zXqDmYmFVL4d8DfD2Nrrkb3J7f0mPQxRwsjsc4E9d+Py48aUG3dxjQq9tmccMayHR9Ls5HC6yYa2zp+dsYtl4Vl9gryptqkwGAEMIPYLV4bSMb2chDLG+6TeYTkkyiFkosCVoE5bFaA6eN7dzUVMWxIDuKWqK4Fn/XH2whDPF7LTMFzBpNa+S3osbMj1UTVoWnS5OYUKHZnGFn7N9DjNozM1XcjscaJhXyEz1HzWxAsKwILSpqm1SYvaivz02jWnyE19m0CFW8ln43aippewuKW9yqGyx4Pah1QyugurlApfGFfidqQgmwIK9d9yJYNqbQe5AngfbJ1XgNTzeRmkQGYOvpGC9g1k/U0itvzBBoZSlfeHHSWNzCYApDQKbzF2z+9JqaDu1uvCZmzgRAdjNm7GQa56Dbrc+kyy2wPASHRnANnSwhC7XY0vQwX1erj1RzadssGF9/S7OMGN/h7/NFB9H7TqtLugG5rolmX+9BW9s9KnRdDYX+fdpadmlQy7vZr80yTjNxQxXnaPx8tLwZ32OM6fWQN1tMZiMb2cgvMHm4LZkhID9pMNSFxUcIuKOV0VycelZCtVw2a9AfRB+X8QMMAfmi0QNrRqhl5iczy4QW0LA3RqYarbkYtW553Jhmyg7VoqmYiWh93OonN/sjzxypf521A6rnD+Nv9ZyiJko2ayyOwnTlMCrNImF2gb73UE8sRgCC5kalYYRyZhL2J4bJ4dgyjRUNdWGaz9KyL57g5O2RRYJYir50XArjP7TcUGUWu7nexfjE5IWFn4txhtY1LTN3y714Td1WZRZMcVWzYwdjtxjX0vcoc8vwMCMHAP25+H1aicVp65aO3m/GZCR4bIX/NwcTw10xXV2+cIh+P14zrS0kTCTF7TjPXCdZ05u1VBxq1u/RLTtn0BgRM4LSB3R7Gt/SuJUse1Q6pkFhEDzmUOdnUu/FojeL39L4Iqh1rWENtxPqVQzOvcjDvcmIIOQRgUkXhwu25I09GNuCZYCv3altkTEYXBwufYEQqEfTMfMUdshjQLS4eoTuYuTAym2jGsy14aIoDn2BMc3KTaCYdRaY5QKQAYanWcftyHaF6sZMr0uxFMve3KNBF5QkSFHDX+iDEUTMhG4Ppna9neIvOI7iOC7Sdn9sbg83mX5aGYBtcj2OZ35+y1KexIVw0WPR2zhutcSAdGh3VttoMVg5f2IH1S3FkRh+p7fX3YFuFLPO5oYgPLpsWR9svtMgL+eBbkd9c2nuGjduujXddg3pNYhOV+fE3W9irZrH92zsTIf3OsdZGxB00yKeJQ36Mohd68Ypwe9ZCsfgewYeTa6Fm287TSAMGjAvNYWNfsD8rRH2UMz8/MNE3a+BSHL3Ham07lU27tJGNrKR+yoPtSUjQ0A2a6M2ICJTLQlqJ4LRUsl6Lx6h5stzQavAK0LDqfERAlqasEwdP7Jrpi7TvVk7mLZtNO3LtGJxe2kp3RT+z6Ax3alur0avmrK6QZSnAryOlmb6E4Y+lLmN04+rZv+oQHkSj7G86PBxjokaMGsCBn2PMHQGh0MuyE7juRZP7OpYO+QK+2fAOOvcgmHKndZQmBRm7R11ilgdu5uXWk2AAu8YyG08LW8paYX/tzsV6n/38Ti2dz4Z5+y2I2npFrTnNKB82plLNrqiQeeLUzseU+pmaZ40Sd2WzlmemWvB7xfHDRpdO6BlzHT8+YnXm+m5250KlVp9fp0aJC+8NMXucV2Y1UJrvJvkKG/p2tZTE4YARPcSAAKD6YdLAzjSDeu3a1s7hv5WD6Aflwbyu1fZWDIb2chG7qs81JYMhgGybNDvbFtAzDS9BhqL52+iPx81sCRgpVKDkubzLzqzYGhxUKsXJy3CfBV4l88aCxRSS4dcTPPRx0bhMQMW1hEu3lcOnw8J+Iz1V8PUNSqgWk99fwL78kVvweVuohaEatjq+inmT8T4DmuXmt3Krplxj5ALiiO1pDROYvUt3YCgxzdLadmhrxnTUA2cJ7EjjYtZXGLeWUzhtFMNW4gXjw7xvW7KlGwP1kYTxDe62iPTC2NcIuSC9h2xMYLV2bA6vOnQbceYxvRnYrOL5VsvoNWSDmrsfNFb0N1icSPGPSoDvLV7askeLiG9AicvMs0utt5ataihcaNYCS/2GojxokatU94LJgzyZW/gx8BznvaAxucytbrGN2YOmGTVPWNmk8oLI4NX8LOshpZaPym8Hu1qDPwzsSBDOFsq/hrlod5khrrA4q3nMHruCM2lVUQksw3tk+d98bAyddkDasLWL2ptx6TymhRdMFbRm1RcF3qTh1Hp2Ak19xePbpnJyoXIwG43LS04aFXYeeYl/irFrLPFw0LGQh/4blzYA9GP1KW6PkOrGRPWLPVqKvfbI5Sa5SLqtRvnthk0iiPJl4NthutC5HQccPyv2astk9RpcHAo5QwlgbkaZW4b8UnnwV66spz30YuKH6oLNHs6tna1AJLnAmKQnxsOK5yn1/V+bo2Qa01Z85aIzSmOl7aJMoOEXGw+OA7eH4Rg96cz3Ejt7qi6hRJgBYl8aFkRnTWeDGCQt6uLlXuaSr7ozH0zJHkIK0WhQNzgeA96Fgc/E2Hsp28/h+lHbsXj7ys7wYURyhMdB13jZrA5Cnlu7wFAu13Y63uVjbu0kY1s5L7KQ23JQKIG63dGpmUZRKSpDuAM18eQaA9aDUwXA0A+Uw3LdGGdG5LXTt0PFsgj/qGYdWbd8LgM6GbL3qyCzDAxPYTmuJrP1fVTM1nJpcIA3OiFY3N/WN+0vDR101g1pZMdDeinxJkodmXZI+tY8+KakpZCymsSvz84vYW6VO12abVLtEaCuDY3jUnoRSHoleZg3jm1Bd1GSs4q+IRmgAH0fMstwfqmcrscjMwiYEq929MgfNNZUJXz1+3UZqXSiitmnVmsVnVMPplpaa5LZpQSmWGP6IKmPD/ErHiQujOXaLlDJHlj88vxZ/p/s187AVZCyMU55ZpcgT+oLB+LYYH6VoP2YrTsnd5hsHNwXkIuZtlZ1X1B93QOac4izV+LbCyZjWxkI/dVHmpLRtoB4xdnaHdqA6kZ8pc7/nGDfKk+t4HxSpS2w6ul0WYGuJo/HrUAeV1CmSfUiRr8XHRer8L/8uwMDSMBTflJY5YMg4PlUYOefCWqRZsL05Uq7Xgt+ruDiY2JnDTlcWva1mIiLKnpB0/PFp7epkZj/dPoxVMbr6XjCSqcFig1YE2LMGsHi2MQzZr1ozMp9PxqjBE0TxzYZ7NWq9jLzLUzq6vVCixOWywu6PWdDnZOWkrLcwSQBYvBjXReDJyXZxYEtpjIkNs1GLRBBOWVIwDA4snYlppWbdYFA3dyTvN5Z/ePwdKhmlrwnYF5t0aCfb9icH27dAuDMT5acWVmlpqd5+bSrmv0kavxe7tT9CO1TlkjpuOWtkdf01Lz+N86a2O+6GwuaaXmRx63WSoKGXdtCP3K8lBvMsgE/bhEMWsNY7HO/rU8GFkgz1yC484WINGgIRd0B3Rx9MEkUjSh5uQD0e54BJ/n6qYliuMkE4SEkqEuLDBLDEO7XdoidkyOZ6t4DAas81lnMH7SAHRblbkAZqLTjduv7fjMPNXXeydiovtzzkv6jVpBCXer20svtmPaqsjRTXTR6xzJEGyDt01g/1L8vwv2AMzbeKztGzOcviW6fuXJKl5m/tjUNqDZRX1Ab/fOJ0zO4yRYzc2cgdTRlRPMda7KGQnBcIYCtd0qUNxiYaG6Loce8CQFAt23rBtsftsDd4ltjoi7mvmaMEoGZiOXnoXkxsmEQjHvzX3j+uonhV1XP7qo/2d2/4wadu5us68hd6mI9SJ5lix7uy4vhyBeqjeX+F5l4y5tZCMbua/ycFsyAJAlJDxwq4IpUAmJRhg5nmR0RdO9ursPdX6GfMoCv+LBWnMTTlpL34YJuVsdAcuU6uj6ws5NC4bpyPLEa29YbBlNXbVIiN8AMT0BtWJbrBCzHSxYxxotY9YXpwOYfCy6BKHOjQLBtOkoQ6lmsrkRgRZQifq56PYMb4m1L6PnjnD4S+Nr4nVkALqxppGfo8VW2jyyLmjRKGJ1pzILxtK/ahWNnz02KyFPKCVs7k9ZkxTMBV3vBNFPqpW5B6ILaxzMtCqaDMtHtlfGQSuq3SrcgqF1mNCRWkFikSXI7dUAbRB3H3n/IX6vMF3leEYBC1RzPXZjT3m3dL8Xg1kpRlCu1lNKgG7dLDKgvrHqhg17leG0SANKIvt2XN2RjO21yMaS2chGNnJf5eG3ZIYYizAQkWppI1oaFQbMy80aESPOZuqx70urRWos/RdPEYos0VpKIbFXYaSoXtat1NdmRnfYPxI1Mat985nHgSyg99IpQBJwJZVq9gpUhyTFJhI1/tclaXley3K/sqpalvUP1MiHLUSJuEi/UB41ToBOaoWhRLu9SkJO5HTWD2gu7/p4Acye2rV5oBaVIWpXADh9PF776FrUnNVp65XFy4RK9NloIbWPxmp2SyufZhZDonUhfTBU94J1WMfteushgxWEKvOx0UoQoDSi7No+k7AKOmOgOKszsxJzXU/ZMnPrN3MdbWT1x2zFM9H5yb11CccafA0wQNzsxvmvb7dJFwl2EqjNkmbtXDfJLSBvUAHWVCX9tniPy5PWgvsp2NRAqzoes+aOO6NGuVfZWDIb2chG7qs81JZMyCRmB+a97br5QndzpjKzBNqfEHIzmt8wsyJAcZvtMuL/ldYMtbuuSTI9bn197rSRqihnT2yjUhg/05WMiUjwDI/52il5+ZKUla6BSypgpsORAPRUqtuN1+uQqFrTl9IHq0+aPxLH2uxMUDFNWTpUnVYIW3p4H6YeuYL3yK/D+QI8BjIUMJVVanaOIL7qdovlgcZnWsYsBPk2KUFV2zKeds4h8M12bseqDlfNluVBhdF18uSszgsGnw/2RyrmzsvinT2DXcN6WUR54lmjLuG+odVsFfaHc/R6LctHlAzNYlu+7pgNQi4Wd/H+VWqZ7pWob6xmKAG38pgtHL00szHRchXNLhWL3o5vKf1MnDtJAaJh8LFZlrPSbGiVrXYnvQd5qDcZGQKqoxb9KCA7PIEAACAASURBVDeXhQugN7KmFu0OcST+O96ErGdQrsSwpdy3xIhoEFcSrlcL7E0rL60PfNDEcDfcgKzbYAiGZ+DxpR2wULeK2eHx88eYPRndBz44SzLJz3q/BrKijQvHcOiDOX5OXcWt2sxhzktxmpASJenNwYLHq3VbMgTnoU0QvJ32Q1rhx7VNUVOqg9f48CEKc3IeB+teSPMduY+VAc5CN798MXhHRaaHhzyZI3Z9CPZ9ejN84LNlnxQkKhRhq8BIO0DYZTAtn9SVNTvx3OOXFmfaHnf7Y3+o2XnhFjs39JaEMJTvaWtuNDdJb5uc1MwxndwPkEHvD6kYphUWunGPX9L7zYZujdc6EToQSm+va0p3lDuKmvAKfY76Ordg873Kxl3ayEY2cl/lobZkQiZaRzN4z2RNozIY2uxVVqNDjSlDMJQs3YmsyQ14RVO9SsrfaX1YAHOnxPiFWPG7eERpErvgZEHE8VErTQt7r76mrsDByKu2ibg9mFjqdb3tbD5rLUCXM1VbZeb6WfX1mL2FBKOrSmlApvo+2OdGUDXObb5SVG96XiCx7LqAkQLWhqTOxlKeZvG4NWSBy0MGd5dmPRlhE12TaW7u5rJ2NDPJvAateZp87NCqjKfa+eDkrUptcavB4qKSkGkgvd0t7TppAZUnnc2bAflePLXxG7hOLapuWloAOrDvUp17b3CC2xJLkGliAuSa/dpq4eiO2fod5ahemtu5gGjxtvtMf7uLWcxW+YeRaRfK85V9Rte7vtGhVYuKlmB50jl1iNbO8d6Nnju27hT3KhtLZiMb2ch9lYfakgG0fmVImokz9kCrpR08dcyK3mYwWDnTixKCg/bUt23fEmMj9bWFkV4bXLsSzC9rqls1cn1jZt9jbGAdNh5/6zSShMOzz7QM3vWRwUk2tQ95dsa6iZ0YlWPkvProVwnYyy2ewhRp1gazONL0swUxT52sKv5AbI4qJQ1fXphguc9q4Pi1buRQ+UFXVT9ifU7SSWHkwXVeyUJrqIwE/qTDUgmbOMaQic0hyZdmb931OdLv5QkHisHsk75YVvlN7Nu8R6MxOwISLZ42752fSPl7mkme1Ay5NUfr0wLLanU1e4XF0Yyv6LjFwBKQNfBovui9LMPAeJUB6XhfhjI3K5jrhTG/vs7MQrE1PS4sHmXXVGZ+jslqALi9MLG5v1d5wzcZEXkCwLcDeAQxXPa+EMK3iMg5AN8N4C0AngbwBSGEW3c91hCDqUOZOVZEFwNNzaHyiTSXIQRnt2cm56RDt+ObEOA3Q4IXMFo7kWXlqE1iF7Yrc9dYyMgHqE+j9WQju9lYVomZGBlgrVfJnpYnD7zx0JJz+LTzViHzVTRrSAin6pteq7Pe1lQ6Lz4koz4Xbtb05uo055VMa5rbJuoEUn5dnD9+R4JncCBsjpfZtVhLFD4Yyx61ZvrmFyo/hn6P9VsAMF5zLcwdKjNze+jO1tcW5pIx6L08V5lrZvNI5Oyo8GAqr205WAbROJJng41p/d4Vuj7j8Rz7Y0purSOADH4PuDlWh41hpLheQiYJfmp1g6huNba+2X0iW/RWj8ZNdagEorrPiLIGP6a4XrwneRDuUgfgD4UQPg3ArwHw+0XkHQC+BsAPhxDeDuCH9e+NbGQjD7m84ZZMCOEKgCv6+lhEPgzgMQCfD+Az9WsfBPAhAO+9+8Fi0GuYeODSgo6qpUcfu4lhN2pg1tmk3KV0D7qp8+1aSpjEUAk5UlqTYnQS1jmgxvjZyM87fzwGII1qIRfHfmjDsna7tM85/uKkQXOwyjHMFPxQitX7GJ9visOYuVsVj9WeIezqpsUZruGhzMz0Xhxo29IXoyVW3J7j9JP2VuY2XwwGEaArt9jL7XVOTuKJ1y6Vt5XzOGeQ1c19McvHcS0MClObShfsmq0HlkhCCbFa2wMByuusy1Giqv3aXC5at8XpHVwiknSddBgGunzuGjW7Tj4FRApSP4daWaS4nHU2bnPfstW6MQBGk9rsu2XV7qS8v6sterNmcA5qZuBZbF7llrrmug252P02y7EHxs8rXQWryEnl+dLcOzDcozzQwK+IvAXAZwD4MQCXdAPiRnTxZX7zFSLyEyLyE213+kYNdSMb2chrlAcW+BWRLQB/B8AfCCEcSRKcu5uEEN4H4H0AsL3zeOhHBcqj1nsgKXVlrvGS008573Uc7NLYB2PKhyrHbpwhX64Gy4jGDQILRFIDUWvzeECMX7DrH60hS4EuB9eeJ27dEH05u6yxm1Fu1g3jAcb/EYKnNTuCt8TqtKw1774ThPN7ZOkP4hZMniBXKyVugigQUMe9fHTb09tTB++Z7z5lJbADv3qNTxDNWh12WGrtVMh1rrokAE3E6sDgc2/af7FPi8lT9YxVtFuFk5X3q9ScQ50bLWrHIOtyMDrPsZKWLw9GCNl643q1wEqvXepyR+2udxgY6gyBVqfWgBFV3exWSQBfLZ+t0gK9nFtaxdIHs8ZGGm9qdyq3pDQGNuQ5Bg3mMxbHtHioMiOf4nnK0y5BZzuhFjtzDAnJVpTK1v+9ygPZZESkRNxg/kYI4Xv17ZdE5NEQwhUReRTA1Vc6TsiBdjuiFpePRveEk3T6lpj5KeYDekWnljfddGRwjTe0PPGFzc2GKM/qqEN2pKZ8EqDl9zvNNlS3Gzs/Td5mz7NXRAPL4MFHbho8Z3Ha2cNpgTd9kJZ7lRNqkR4hOGx+PcDdjfOE2c2Dq1zY3GxCLpDLOzamdB7j95TA6tAL8tYZ47LOqQ84pww2V7cWmF/Wzbcg8rj3zNvMN13AXYL4Xvy/T0pCrLVIOzgDnV67sdBdmxt6ldeSlmQwUJsWE84vMVhLN6QF5vS/NQuzVTpdBLtgDJkpIc6LlaF0jl42HNasM9eZaygtduTG7dQgAdmMGBunMOGaYfIiZ/udTKzFTorg5jksm3bc2bzSbeO196PcM4z3KG+4uyTRZHk/gA+HEP588tH3AXiPvn4PgL/3Ro9tIxvZyOsvD8KS+XUAvhjAT4nIv9H3/jiAbwTwPSLyewB8HMDveKUDZV2I2jIE41il1k21o+34ZpY7PeKgGqicDZg8oynPy9EqGl115v7FBRb7OQcqrQpqtKHOzXw3lnu1Ksqbc8yeWA0Gy+D4lcnz0XxfXBqbNpw8HV2Y+RPRysjaYA3dy5trDcbgFkyfcNRaypM0oFXm9SykMVgO9htafYVy64Yiw+gGa2PcpKfZzrmV4J0FmOLleZqDsRchFm4heQByNeXd15U1dcs6t0LopuSJJUZNTWuOeJVurzZXsVRCsMXF2trecq7yWevtidXF4FiXB7W5xQzWl8c9lvvKU6z1SXkzoDpiQJkWrxYvVmIFj1wTi3Pu/qzXQcU2u6sB1+qlY8yfjHQbtp5OWqcGVcuKFnDWBQsyg6RozWCW6+QZXVePbVtoYGxrPXFF5SHFyYQQfgTOrLEun/VGjmUjG9nI/ZeHHvELxLQbia2tTmVKmsLe04msDl50Bm4ywFgXLGhr5FYj72NEDUgtljeDayP64U1vPjOrchk76bdq88PTLXbyQgzuzR9lvyDvBMD+S0yPdmMnQBoqrRjvA3KmPIkeJhI0iW2wPmhxUGB8vbdzATGYSMvPQHaVa0D68LRy6ltdUnHN1C4QiPQlgI1B2SGgVgKrXFOk/biweSONgbHozwcUi1WwH4AzCNTyuHW0K6vvNRjbJpCEkHmAtGXwWg/b7BYOYjv1YD4QKSo4D1xXfe1wCVqw0oUkJb1a+1UeO6rbEg/z4Uy8w/pGbVXe40njTcOoSgi/iSSufRyspDYQoaewCRhtt71uy6lNBwdargFRi1lv579X2dQubWQjG7mv8vBbMlmMjNfKy8GUHeMC/Ti3+As7A7Y7ldMlMo5R++7PTIKRLN9cYLi0SinZTQvTPvkiyTwsqQk6OxcQof6ZkXOrldUOBiZjFiVvBq8fWqs/GXYdvEfpa09583sLrWGqb3ZGCNUqgKw+dKJyXmcxS6DvazVAISGgZk1NEDjwq3Y9VR6t9v9h7VJ12BnIazLRqvfW+4vb9Vktlae3Oz1GOUuAaCdKzrXnnD7GI8TyiHaw+h2Ota+ylWp0AKhPO4MzLI1kS8GEZWYgOMbiZAhGpLVC8arlJLS82F2y2Sm9tzZrzA5K1Ld0vEa1qXGspKKb0hyMnJIzKUegdbrC6YMYR4uxHcT+YIjZJUvHM4uakJHT3LDMWTcgD2sHfo3yUG8yRDH2VYYyZ93JKpFU1gwWcKMU8/4MfUE/ytyd0oVLZGdx3HiD9CnJlHqjBugSagWhRzSs0kukLGfcgKQbULEvUpKi5WIMumAbbkpLvxambot57yldPdb0OaUiyDMsznvKHQCCBCeJSvhirchTMTl84PLGF2J17C135xcZ/CSLFhxtzXXbOhSASOXzW6d67SNI0vgdcHciRQOPb+q4rjemQHhNQwGUJ14fBSQP4bhYoXPgca1nkt7HIel0wesrb8Yg/PzJXUw/FhHc3Y7jjEZWB0YktmN4MNBtcigAN1huQNJ7kWdD0rK51yQNa6n9vsps06eyyJaDF9oyXd7wGKVtokSPy+CIaeMCloSSgo8NW/TWxRmF9lpl4y5tZCMbua/yUFsy0gVUN5vY/VG1CrW09ePJxHhLPXiXQTrSL2qtxvXG0tTNDt8joK52F0Z3/m6UmwZx98NN6GGdcGrROTP9nrsdVi/FIGnnvYQYzKZFUMx673zIFHkzmKogIGxEjSaeliXqtTzpzGoyi6rP7Bw8t/EcX5tjYN8iTc/209LaxxLEFXLXipN/fwMAMP/kg/g78bT9C0cxjf/E0cLoN8n/S7ezH2VmFS13SKOam6XRG2dzZgFUu/+9ppVnnQVceV+L2eBVyRbgbP1+kCn1IoGcnVWeGyygDzbPTlAlgJZmsd6IpFH5ovf5JlXFcsDpE9PVY1Tu5rGSn7+TPiSgw3ie0cdvW1td3jOMHdncbjt1BKCgUDM6CbnwflG03q0mbtZ7t4x7lI0ls5GNbOS+ykNtyQDRF1/u1bZjc1enVq0OW6snoZ+ctQPmj0TtNbmibP6XaoOdG+l15v51SqcI+I4PJGnccW6BXNadNFoz1I9yj6Mk1tbinIK8ThmQFDve6JqXQcTv5xaIpNZvdktPO55SG3nKlLEHoyNNApGWbgdMy6W9gQBgeWlypparrzKvKZoxOO3p0uWT+zbP8Ydi1kQICp675CDC0bX4u/wk3ovZE9seoLVOAgn5VNLUntq2vsH7rx0qt3KzPqvbHrdZapU5oQPNbmUxJNZOVVcOAQCLp/a83zlLNaZebc7YSb4YzBIZX4lgQPb/6ke5WSaMDfV1ZhYmSzFSqD/vI+9FO/U0O2ODzWO7Nn8GoSCd6sRBoZaCv9VY4D4VAzuuBZuzprekxb3KQ73JxJYoZeSQJXqUi4KFcknNhjV5W/SYPteuvFfd7uyG8KG1orEQLCjZJ7gJw9MwK3LaewRf60744PWjwjYIyxScdpi8uGqmDmV2hqGP5w7ieB0+fKOrC2O+z9ZIjIobS+Rrm2M/zpBrgzUuzurW0luXsihTMyxpJs4WcxZsU6yO4/+zi6XX1egzYujh+WAB8Wapm8A4O9MOhkyDMgTn/2WMOqml6ojqPu0Nrcux8YGubrfmbvRjdwHYHYAtYsqTDv1aS5nunHI2t45Zous6efbUW5HoHpovfZPp1pIMzXZmaOC0zW5r2CMiiTUbue34Ia7D+uYyab7myo1tgYm6pkgfLKiPFI1OjmnNfC33SmRk90sKeYFYxzW62eD1kI27tJGNbOS+ysNtyRSCZq/A+MWl96phSnLiKUqjzExSckaZwLRpQmMQlCKSFkTk+CWZk1IEnB8bJWfIlIhpx2kpqZ1nl6O7VN3uTIPQQmq3CnNjaEov9yurhGWwtkssH+MyJu1BmZvW6iume7UOpcqMrIpUD9Vhd4bY6+SpCbK1GB/Txf0os15N1S091u4Ieav4FbXUymPHDWVqdTBtvTgorSJ7a8ruCaXRPvCeGd4juPlOvuDUPTW0cXAXwahSyb6/XRpOhzVGGDyYmnYVsOC7YX80KTAtzAqePK9u9aNTQzazPojueJwHR90CwOTFxgP5U1ZJe8CcyQiuv7TJvQVtd6s7Ym1IGUIr3o+x2oqWc2bnGhMNP6BQ+gnjWVbjZfzSYtWdvgfZWDIb2chG7qs81JaMdAHV7Q7SByzPK9GPBhGNzKiAaT5L+7YDSq1mXlxgp0ePpxA0VRA1PCnNr2+D+uOFWIN71gzlCyfkpu881vG0W4X58Csam8E9rZCVEJxUak5kqwO7LAictLyltqXl43SdGUqShSd9mnid1P5bz8y8KTwDvvurlh4AnLxVU7uzAYtdxnVqHb9g+qKTYAFujUxeWlqsrMgZSPXUrqFqmW6f5F5FjCSNS0spIeyyeiarHteeQsctspapbg2EN4PNfWPV+oPFLUYvxt5N7bmYtq6OW59TjeFUR/6eccEcd9ZFgtdkFJpbRUIWpXGPTMyqMN4ZI5Ly+eAaarcylDNZuZZ8Phhcgla5paNbb8ebotgZr8o0hlMddzh9Qmvm1roW9KPMKEHvVR7qTQZQ8qjt0hbl/FJc9DTPs2WPxYX4nmUFCrHALG05CQFDtmrYdQmTvGUGjDu1P5O5kT4Y0ZUxnuWegSAmJ22X6zB3mt6l9y/WhTd5ThGoj44tqGrFlsEzE+MX1Z1hE7YwmGvWJziZHqv4m+WFkc2fNcebO+8uNwuH1gO5BqC5wWVdbub69ONKmaEm+FAWdi4CSoJ4CxUz9+kSDUC7Q8Y9BoADusrdjXhyQDoSXelPS8/S2DwSgdw7gdT0Y3FD6fZqZ9rb1/Em5Q1WnKo4nPy0tVIA8iA35ypTTJPn4/VJ426TlbWQ0OzGAkstU+F9seD6ECzzxU2pnGX2msH0rPXPDe2cFOBSSTDbtbjkmSXez3a7tPtIheOobcee3ats3KWNbGQj91UebktGUlqB+B8JlmhBDKPc0pZ0Q+pbS6s7MbcquIY0yyTpFpAfE/EbvzMUYlQPHMNQZmYGW9tZo+sMlmq0+pqR1wyF3FGhLcek4r2CMkxeUizJRcV7XG3MNJ9f0joVBqxvLQ3NmroYju5V0/5W45p64e5MPNbg9Aka4By/OMfwmAa76bZ1fl6e0zoZnK8QNL/e9Sye9A4JRhuq9WSLS2Mbo1kt4q9TTIeo/zNW7W8p7RAw/lhs2zV7W8TtDHlmpFZLtbK6aW4uNuuCrPHbrLd5oaY/fXJqTeAMhdsFFEtaokw1M9DeeLM+tTzYxSEeQ1+odVsseoMkMG2ez9297yvlgh5n5hY7+ZcGvaeFWTDL82xK6G6vdavYLgzpa7VTuh7LFw7RH2zh9ZCNJbORjWzkvsrDbcloCrM47TB/VDXHTVUJE98/m7Vak+V+7SlBo8ssDGnJOEao/XcGHCNtZ52bL8w+R3kzOGKW3zcNkZnWYgxFEuuJtTQhz7z+icE7AgaPvJtjfcvJuQicM1SvUnrOHh2fIenOF52RRDlRUultfVXhzR6NGnDrmZldk1Wn79eugVXaLUHxjAYxaZkocXp56uhbBn7Lo9biEZ1aSLT+qqPWrtnIqwpxxPSpg9vGV051TEpFSRKm7RJ9GVGxTomaG9ygukWw5tjqjTgfRsMZglmpuZoa1WFn98AqrRMsHOeeFuHyICErI/F4Aqhj36P5o1P7zCzRG6xTK6z9Ma1WueEWD62clHh8/ogiz9WKR+bnZcxpdG3hcRcmBhQc2H3SuTMUEq9VNpbMRjaykfsqD7UlI0OIEO3dyoBfDuIiWKxdqXCNHwqK46UdAwDyebaSFgY84j+UGfptHk/LAMb5Cv0iADTbOQrVYEHBaiHhTDE+EdUQIfN2HIx3FLOzDdeH2qvISZhNH7667T6/rJE0pb2ejDw8E9Q3tDk8U7vzzvhuTh+N2pCWUj/y3kaeps0Sfhqdl9PgYDBOc0pirfCBImcphteD0ZrjdQxlZpXwzKbEHlWr3Tb7Oti4GZegBTR+cYZ+XK7MLS0UIOklPuttvgprnaIW3ijp+c0+2c1g9U8kmu+2S6e7ZMuazNcfLYjc4jaFkVp126sE6O1OtRLjA2LMjFSwJpnY8ZjFY1wlX/SoX4oZyV6tm3arMKuJvcFnl8dOrM70t1ppi3OVxRfvVR7qTSbkgmavQt44PmVgodmRkxcxzcrUY94ONtGUfpwlwbE1RrVaEpSpo4f50MEetME2JroHNEPbXU9zcuGWR425RGmhWsrQD7jZjBAg3arxGfLMm5eRyU8fprQzAV2ddru06HU7pUviJEesSaIbOZSZPWB88OvbHdqpommVNe/4iYlv7CerHMN9VRhT4e2TmLqtx5ltlAyI53O2z62tcLV/yoOgRnlh9AXFyuYJOEr29MmpBT8tHd8NltqlsgiZB8CJyE43BTE2Q+cG5oOZNmQrydDX6kOqhbJNWSaFq35cEm8RjUxaCgTfLFKlYRu2bgLtTnWm0aBhubZy+54FyQdY7yv/XW9dGLjWu8w3sw3Vw0Y2spGHQh5qS4aUj9myP+MW9Fa7JChYu2JdBktr/UptlLUB+YIagehhBVvt156e1SBh1gbTOJbyPFyi242a16g7Z4k20O9ZL6Sk4rqrnKiKoDerwl6ye2BtNS/jl7S/UJIOZctbBnbL0261awOiRjRNRqtFXONZBwP239krzGxmNXFfZ9YXyZj42wQCQCsu6fVELbu3daTzUtrYSg1cc96L+WDfNzdl3hsQklCEYuYuKAOtvI7yqEOglaNautkp0RM0pzU7zX7tUIRyzUrMVs8VjyVJZwQSarXmDpbRS7Fgr7SDpcYJpCyOW+dSXngiAYhoc1omvE/WzQFeixQywfgjkRyM1BpMWIxfXFi9lI0xcQura9FtWj6yZVYWA+GEbcyemp6hrX2tsrFkNrKRjdxXeagtGRmihluerwyMxXqV5TlymwQH3iUgrnVyq1CIk3+fsiuhk0djrWMiQrCgJ/3w+WNTj20crdGAiscvrLf1ucppD8dO6My4AWMzrACvby6tayC1fnlrgf5yjHOcPh4DgJPntTq8cBpLjiNfDpZmJb3oUGVejT5ZjRGl6Xu3ynLkC301Z6lGZQHtwXoraTD0sDVypisakyl3vWqb6X7rmFD7fepViZeHLYqkopjjNivydNUi6CuHAuTsPXRuZDEtpn/zZW+lBuPnlDR8L87j4nxlFkyp7P/DuLDvV4kFxjiRVZSz00TTI184PSsAnD4+wvSFeDzrZpHUiBlZVPB7wN7tXHP9SHD6Kefje+vE7fuVWUrTp090HB263ZF+kZF2BxEygL581KlQ12EKr1Ue2CYjIjmAnwDwfAjhc0XkrQC+C8A5AP8KwBeHEO7KmhNEm22deEGYF6PpptAHe6j5kCwPSkOxWhP51k1LmsjWPL0PCERksgnasrOFSoTr6OrS6qTyxmuFAA2gJs3igLjp8DUf0HY7t6yBtWA9T3O5MkQmN4p2WnhbEF1ki4t8gIJTICTuJLETfKiHQsyk5TlpxufT0h4q2/zagFbBoLzeoZKkte0qYrmbFnb+t56/AgA47R+P9waOPSpux53r+O073g5YkdDNnreutSBvEyxjREwO10F12GB2OW4W4ytRAfWjzFwWuhb5kMy98vnyQd768A3M3nYuXsNW4rLkpFvweiBj0GtY1Kqbep0ldVtRJleWVkhpVBXEqYyctMrXxmAtgP18SStaJiis5srxMcwuLbYntk6YvSxPOi/MZYM40pEUGfr89XF0HqS79NUAPpz8/U0A/kII4e0AbgH4PQ9kVBvZyEZeV3kgloyIPA7gvwDwpwH8QRERAP8ZgN+lX/kggK8D8FfuepwhoJjH5lw0k5drKTkZnLoBg2tuuj2GtC0EylqwwsUKRBwMiZgYWG63R6adjZMVCRaDXlVS1Tpn0zXV0rmIkwqpFCe9mfnUOCnpEq0tavp2qzDiIbp3K6lYI3Eq7Fh8zWsvTzobJ62m4sQxK86zq/+HgEwv0+hAe7cIrNG9BrjzkwbtXhzbs7cjw/5BF1YoPgGgGytxUheS6uTExWXQ+4bfHwa0efy0wphczcTEZMvB+i71CbaI18UuBLQ8+k86Z3OUCtPxnMfyuHPuZ+v2EL+bL4fEIlELpcicH/hwNYVd3W7tGNawLmnPm7Yg5vFoabqr3piVZTifUtAPnobn+InXYUqdAWAkOKZ7lQdlyfyvAP4onL76AMDtENiUAs8BeOxOPxSRrxCRnxCRn2ja0/s/0o1sZCP3JG+4JSMinwvgagjhJ0XkM/n2Hb56x8qJEML7ALwPALb2Hw/L/RL1rdbqgdjdj/GDvhYHh+nOXN/qzEpht8DFY9t2jlJjA70C9trtzCwfCw7Peq8AV1mer8xPDuuV1GWGnY/EIBw7GLRbBUZX9L0LSl7dByO5ZscD08Rtb1rU6nxCQD9hN0cFsI1JrhKcIHrhTPaGjqWGPO0AIlaPV9Py1VHrXCe9WzdUikZyngHz89qv6pZ+xnay+yP7XlVEPVLfWFicg2l5AyEGH2/We/Sx1hQ65+D0cm0Wz/q9yJrB4kqsLeu3Cus1lYkHmxkX4cNgHRmXvdf06BwPhRikIEuQuev0meMrcV0150YW/7HgcJU531C1+ruQiVveucfrGI/KOufIEQWPWieDU79eR0Xrmr/RJKl0B/nx87Gil5cKIpQeGGkg/F7lQbhLvw7AbxORzwEwArCDaNnsiUih1szjAF54AGPbyEY28jrLG77JhBD+GIA/BgBqyfzhEMK7ReRvAfjtiBmm9wD4e690LBkilWB+1EC2Vi9l6yMR9NVt1wmZMitNnctk+UhMk4TE8FheZH9i1VhN5tqW/voot0pu9o1GcC3B/5l6HII4n0jpgLGObGyEjbfDGS1HAOD06bl1Ml/ZyQAAIABJREFUXWQz9G5cWAU1Yxum0erc6pSozZcH3l+K0u6UK32rAc+6tdulxQZyy4QApXqqaT3W5Kpm0hgXYfo+FzSalp5WaoU8sW+xKY+BOSye2UGrjD5sDeDI+EHW48x9IftgyMRqqKwxfRcsVkGgZTcpnY6U945N5/tgcTGv3xJ0W/Ge1czgjHKLy9EyWJ5X3pc6sUxUsj6Ypcj2LlaScdwl5RbeoZJASFpD3dhryqyTKcsKprnF8cyCnRZn+jmFTCxLyVhWrfOyvDDC6dt24hd/CvckbyaczHsBfJeIfD2Afw3g/a/2h4tHJhZoI2Jx0OI45OI1I0R2ZuI0mafeLYAUmHwgrWfNyNPK/N34xYWRBrE2qtktUJ6QQCqanUNDmgZvh2rFceM8IT7yAKPhR8JqwSH7EgHu6mTNcKa1LClIi9Me3fYqDiPkQL9OmVBnyHXDzEesx9L/Bqc7aCt/ILqxTi/dQ/HF3h5ws9O5Pe7sHlyYxs3/xu1zFqSnO8tNe35QJDgdYoUKcxlI/wAJ9kB2k9Wdc6gE/dpuWh63Z6gNhtILRmePKyUm3aDKKUKNBOqgts0FVlQ42NrJ2mzlGBjlmLygyHFSbJRitKGkF+V1RkoQLdBMeZzXaECzZrCAMDdpJhZGN70lMpVnedJZSIHuYdYHu0eGk9L1gsGD+vcqD3STCSF8CMCH9PVHAfyqBzmejWxkI6+/vJksmU9YpAsYXV9gcWFkAT32iqGJ302cfpPmdt4MFjQjUI/EVjwu4CnZ5V6ByUvxGHQjZpdHVt1L62l01Tv9WepQ3YR2q7AeRSQUyhpP4zKFmZ92CGqZkEyb1eHFvLP6K0PV1hnK2WqtS+qyEWlrpOfzwebGUtltMK1MMcqM0ivQvadUwOmlVThoPwJOtfUvUaQ8ZrtTmKV5YxED3N04N+1NF8DmpQuefn7MqUdJes3q8awNGCkSN5/FeTz55Gjilye9uZEMhMsQzHUJRsKd0j6Qo4IaPLN7xrazMniNG2X+qFegm5vCOcjE1iQt2Ha7xHLfWwkDfs/ooqey3CvtflOGUszNm+m8lzNPedPy8c4YwYCClm4f5UaCRSvHLNjeSe7vVTa1SxvZyEbuqzzUlsxQZTh9fBIJn8erFIrFzGHS1sfI+FYaD4Secw1F4Fe3pfSEyuMyupVQLk7cIqDkWuvUb1VoNZVZrKeyReycHoAT69RIDZI1WULizSCz1ttsl+Z/Lw40QHs8WMDUUrEJOHBYg4YXx63Fkvi9djuHaKvG0UveIROIYDhaY0xb17c6T11b0NQtGLNCkrQytees1TTqNMP4+ip/C0ngl/slFgeaMq7891YqwlhBgFVan17c1rExxR+sipgauZsUK/B9QGk9ZbXWip0Zy9POrLG0Pzl7ddFKyJrB1pvVvTFgnEtCi+pk7RZ24wudqm7sFi/LQwxWAI97LfdLI0CXfnXt5/3g/DSDx3rWA9AIHkTnWrAYYR9szd+rPNSbTET8DpF9TDeZql0t+c+WvbF+MdMD+ObCh7ZN6muqQ334rMy/90JHHrdz03v+eHQBqtutoW29/5LiWW4vLRjMoGb6ur7umBjbGPRrfFi7WhAKDTyzuLHMrPaHaF0ytnGRAr5Q54+OrC8Si+KGUqxQkw3c6pvEXASLHmaJJd+NiKJ2nEwzXcXaMCA+5Jldw6MM/M4OrH6nVtoNa763DBZIJUq7G2XGOlgkLIgk7OI5rc/UVpHwJvP+BzSj1bltpxkmV5klohJg69jCNlOKDF6QyOZ/oXD6DGKcDPFdANltRT4nGSrbpC0ATbLnYP2+6J5KHzwxkPTAootvtBKdB6K9Na5u4NeWFjQ2xsfKM3CcI5+DfqWg+F5k4y5tZCMbua/ycFsyfUB51ERqS1I9qmlP5C9ysbafo+tOXegUC6oJTzt7bSZjWq8UVgN00QRXN6VxnMJ6rYvx1xaZuxFqlle3nf/VaCUKcd5c1VA0i7tpYVqUYy3mXoG+jjYeXV2ukBzxPaYpGQyuby69TurmauAxXw5OQ6HaOW8Hc1kGQ70m3LgT9nNi7ZBr+n/3XKwWeerUEdPUugzUdtMCg7mZ8b9iMZil1KfoZGbcl6s1SaNrC+//dFN7Mu2PvG5MA6z5okBxy7taAt5tsTzuzSK1VPo0s7VFV7c6bG39SUKjCkSaT1rZtCAW5wrjULYKbZ3c8rBBRzdv7i4aaqckAeJcc31wLVDy1oP7tOylD1bnxTldTEtLKvDeco0Ws3alyvxeZGPJbGQjG7mv8lBbMkMpWFyokS/DGb++u0NPnPyQoKhtVBogXGxpZ4Dj4Cz7Fes9tFtjkq6mv95PCicGV2xWN87M72Y1NmMEQ507/SZBgaPcm81ranzoxawDan8ny04CdBnBXI5Grm+4xZOOFXANXyRVx5WmObOmd4IsHY8da6t0za3o0OVeiW6i6ekEBOdk6PFvpm6ld6rSnS326971GioiV5N6osq6NNIKEAOubSnhU7NbWHyG2nyomDZ34vbDT9Wg8O3eEbMa92j2CvTj+Hmm1hAb0uezDqePxPU0VStr8mLrVq1abN3EwZp529l4gXiPPWCt1sVxfyad3IsCHhOrvGxIY+pk7pyz8qS1qnHOFS3Idlpguat0pz97CCB2KDDwo/L+jG60Nk7rV6XB6W46spT7vcorbjIi8tUA/hqAYwDfBuAzAHxNCOGHXpcR3INkbbCAFrECzAhxwruRN1U7+SWx2RcDo8CqSWqu1vVVCoJ8GdwV0Ae42clRnqzy4RZzR2HST0ofXsOxkDKhi/8AGF1DPy6dLKthgFOLHAFrRMaNqjxqUcx0ga5hHRYXa0yei9miTHEhi3OVFcMRG7HcK+2aQ1auHCtfDLZhU8rjHkGYsfNdnPMsa0mJkInNy85I574LXj7BoCPpD5oBs0tx7tNyj+lLa+UHk8w2GWtTrNc21DkWikXhphSPx8BpfK+Y93Y8rhkGrBeXatSHjIzqsMe5bbaU+lbn7tKw6uZ1k8zGRPcqX/Q2DioUc6EPOzsGqRzKo2YFLQwAfZcgg0fOUw3EDYNuPctQIGLJAuOr3i1Qs6BYEyH19TjWUGW21u9VXo279GUhhCMAvxnABQC/G8A3vi5n38hGNvILXl7NVkVd8jkA/loI4d8qydQDl1BENKV0AVvPaLtSpVHgqMtTL9cnwjU2FvN0HxBTfXRFUuwMEHsWWS2IaSxHqhLx2+6U9hvSWFJ7lUe9uTW0QtKAMrEX5UlnFox1HWCKsh9Q3SRBVW1jpCZbb2w3eWFuQV6mSMcvLSyVbhSN4zwhcYKOkRiawoLXtEbCrniDOkMGe03RYj8ea/oC2+d2lk5fKE5mAqfjMJ5etSrn53Ozhsz12sogU7od8b1yNqwQRwFugbVbuWnuTIO8x2+dWrq61a4SQ5klVBYssvQeSEFfmjuxV1rQlmuo2S3cZZkyCB+/P7rRWC0ZXeJ0raXFsgBQ3lxY0z1Ku1M5jauRpuUGj2BgfkiaDVrjN61nmjzvtXbEFknvxzNOYvaN2q/uTMDyGuTVWDI/KSI/hLjJ/EMR2YaTTW1kIxvZyF3l1VgyvwfALwfw0RDCTEQOEF2mBy8h7tr19Tn6yWoTeQbx8oWneI0kucxWOkwC0Vfn7k9tbl0DM3E2fNaQBJgGZoCzPO4s7nLyVLRMCOzrR9kdA7n0vzOrScog/ep7y3NOCylr23uz41qUGphafSgyQ21yXrpJ4YTjFxgjGFCs9VsS9q+q5QxhV9YF6yLgdASZBYFJLtUnFha17sk8avXthfdMYtqU6eK+FFSk9dTrHd3usdxhRbtanHtJW+A1SzONj4kSTlXH/Zk5QunzbDGhJUF/rfdDqpy6FUlnCX6fY6dUSx/X9PkY7J49GtfE9LmZdZ1I65kAYHlpYvNSq9W62M+xpYFnt64Hs6QZHE9Ru47cjePpJ8WZ3lrFkQfPSJFBpHd12K7U892LvOImE0IYROQlAO8QkYc6G7WRjWzkjZdXk136JgC/E8DPAuDWFwD8s/s4rlclIRfdgccWnTcRcoI4QZXxc3TBrA//fuKLs5yEmaGt3PxeWg3FvEd5fabH3dbvFVZVXR2RJtEzBax/oRSzwVKveVI1TuH5U+h3npAW8TvMVFDzMQ0ZcgdsGYgrF8uetJJo9eCUjEBS0T141TNjM8vtDEO59r2QpGNpQRSsLA6mKce1psbHE7SsFVIrpzaenQpdzRR9PM/ppdzKGhinyZfBfmtV94VzsFhGMAFZpmToALDczS0jyTgKLZl223uE8x7Ut1sr+0ir5K0nFcOVOlftVmHryq5vWiYtcLSWS8sGJAAhoxVJC7z3jqjlqoUCJLxAtFa3cs+66XBSi5dz0OyVll1lfI7WbZByhcD8XuTVWCbvAvApIYTlK37zDRbWLg1V5v2T1tLE3SS3FKnVk2TiKUYNxlbH3gaVHLKDkQIF+8za1HYBA0mkLL0pyFrSCuggk4fXevLs8uFuDVfDByGtjeGiIddvc25kD/p67yQgwQhN/OEtrkcO4dPLB/G9RfCC0WTzXWeH46YasmSzpTt0POCozFa+H8QJmIq1fkDz86UFXA/7tEm9urTq0s0uElcD5Ia2ZoEiMLmuNVq7bECXwAdYu6RYkPqkO9OEbXGptoeKG8X4WmsbOzcKzk/IBdXpKvaomyQQA9YWdYPdB1IsEGldHbdevGuYqewMLQeDsVmXtD9WKea9JQNYfzTkYgx+3e4qI2LeDnZOEoJh8IJHK8BtvZByqeEGbtr5socMrvDuRV7NVvVRAK+Pc7aRjWzkF528GktmBuDfiMgPAzBrJoTwVfdtVJ+gDIVYHQ61uAPaggcF2Wg+SRNnZoZmhhpmwJJdC+aXp86Ly7ReSGkc4n/FPCRdH9nF0euJqEmKE0197nt3Awv8luLalsFXkirNXTtzjFkXTINZJwUGCQPQnY/WFtuidtPC+0nRshOnI2A63NPsjrTtiA7oxawt49QtHTi3XrlczBzQV5c+1kqrk0l2xYDu7FKBYs7qbp2XAji5rFbKId0yoCSxmM43KSMX5yqz9izd3+FM3yXOYXxvNZ2bN4Mx/DORgNItzbSfBq/ZrEl1MetbnblCRpTVBYQ12gWzXkJSi8TC7OWAbrwKq5DgJF+8VwRqDoVg+nTsNNAqh3TW9BbEXug19UnXBLOeSeol+Qpa/l7k1Wwy36f/NrKRjWzkE5ZXk1364BsxkNcikWC5gwyFwcRZCmD9Zm4unaBInb6sHbzROet4kv5CrN/pq4mdhx0bU3KhbC1Ym7WDaVQe18Y6BO+HlDHOEMwK4njTDoLrnRubnXKF/waAx3yQpJ9zJ/w2n7/wmEFfrQa982Vv12Cs/HqdzXZmQVi7luBzOZAbqQjgxSx2V4F91elgsYFbh5F7Z1IIoKA6liaQRrSvCotfBD1PP/KgLWM+7SS3OBdrf6zDYineiZE1UoUYwov3uh85PJ8WAedguZejYqyHsIMCKBWWkHa3tGr7mrVrHlfhPPQM2g5rY4Kvob7KrOzD+mV3ARx4ChnICNcgeT57iR33mD0RkxHjKxGkevrklkM0dGjFrPcuGQ3nlkDQ4kx5yGuVl91kROR7QghfICI/BZxttBZC+PTXZwivXUImkTv3pEOnWIiCRX+t1qNslUnLELYY8RATA3UA0Ky19vQ2qiWWB8wCxO9KcBZ/a5oeHH/B94wc67gz9CURozIE3yxU2q0iyUyo67LkQhicSoA/62TFhAbWibWIMvZCQmvupf/NLlbGascAt7l5IobkXe5r8HsLCCWpL2DjabfjecfXtK5q7pgOurEHeyc6j3ueSePmPnFzv5rTJaLbJKiO9HtTYlZgXLmkqDCWwM6zeSnPMe+jBTgXgykVzsf42ehqNJemTmR2TZufXRw74dWuZ5esWJaYIm4ipThxFDePrdJIyhYXtSVORbS2801zbpfbGabPnuo54xptdgrPVlHJLd3NtmD9ruJxDjsbBzfEUIgpzyHJPgIaZihWlctrlbtZMl+t/3/u63KmjWxkI78o5WU3mRDCFf3/Gb4nIucB3AghnLFsHoTIEFDOOswvVYZK5a5e3/SMO4Nx3LWX+6U3K98hl6w3li8PaQ0x5ef9kbxPElZcFUB7IDEVzepa8uOWpZnQtFBG1xvIxAPUQExNkmKieSq6azXpICc5lgderRvHEdwyW8sVZkDiAjrOwmqRRh7otGZqDAAm1gKxH50W9BaDYBipWa1WQCgG4+PlNWdEm1budhwr4rdIGriRSMrqoCqgJTqgYlAWWJzTlP7N+Jn0sEp4Xgtdneq4tWDzUBb2Hc5zlsAJSNzEY8zV1Qi5WK+nSUJaxm4Fo6tOu2F1QWs0lpH/l+4MLdPeejwZbWipyYbZ4A0Ea7qAg9W2UfJmWIFT8Fri/LTIaLlue3td3nen2MhsXUyejRZmu6f9wmrvOXWv8rIpbBH5NSLyIRH5XhH5DBH5aQA/DeAlEfmtr8vZN7KRjfyCl7u5S38JwB8HsAvgnwD47BDCj4rIpwL4mwD+wRswvrtKEMGQZxjdaC32YITOCWm3cXCMXDMwZWfd9y7VGF0jCE/5SsjqP++RdlQEokVgACwSJ02cM4bbNzXVnUiZl+cq74GUdDlgXYsFfFVDSXBQYErzaL17lmup2C6gX/PDU41Wa5Av6wSLPaJi9XtqHLUTsdfNrltb+bbWflUKXKu9a0KzvXatAViq1t2ZsG3utmnUk8vxBONb1OoAJzxMSJmaob65igLuR34exmkYZF1UFdaxZMXCA+02pwPO3FtLYS8HiBKZs2I8awN67Q7AezdUTrtq6HLyDu0VZm0xfjS60SZV5gomtKrszAnQkza0Z+IupQNQxdova/1eLswtWAC9mPVureq6Hd1oDLQ3f2yqc6sJk9vtGVrP1yp322QKElOJyP8UQvhRAAgh/Ny9Mj2IyB4iAdYvQwyjfRmAnwfw3QDeAuBpAF8QQrj1SscKGYDBsyjVTV3EGlALmX/WW3BN7AYRRj26usTiwiqnKRfbfKuyjSQk6GG6IkTHxsZpxHoogpJ4kySIRmaykHvWgpmN+UGO6RUNVCs+hoHLfN5j2NXMhxZNtpMMuZrX7CBAKoTFgbPx9UmGiJtGr90K2qkjoO0B1qnoJsE2zOGtMViJusWvefR5AMC//OinxXNOk6xYt1qsmC+9Qd2oUNqFXW/Vyk2PWal+5FmrvNZiyAuZty4ZObT++Ak+8PH7DFKH3OeZG1AfPMOD5CGcXGWRorIUHummsOc9wi31MfgGZX26j5xO5PSxVQWRNtMLmgFLi3GpXCghT7JsdLWLwhvaqaKcXFkkqHYmFLxTBjNNKe+zKWJdw81Oaa65tY8Jgx3jjehWkBbazNc+u9eYzLcA+AchhE8F8E4AHwbwNQB+OITwdgA/rH9vZCMbecjlbpbMO0XkCHHPH+tr6N+j13pCEdkB8BsBfCkAhBAaAI2IfD6Az9SvfRCxR/Z773asvhYcvaWKdAQa0OtLRdq2XlNjRWU0h3tgaXiaeKzZ+bEXB65b+xkwO88Wt45AZcrYyulHTqNA7Sys1RncgqBGzvpg2kqS4r+lpilpBp9eprb2RvQtCZwWSTpbheeR/iw6NWRw1HJyvR4UhI0XAPKlYHku/vGV7/wQAOCv/PRvRKZfaPWzg50Zrp8n6prXTIsmGHL3XZd+HgDw/l91EVJqirmKE1hV8eS70zme2L4NAPjyR2Id7s8vL+Of3vglAIDnjvcARNqI20tFrzYEMDH/L06FyVrBXpB1NGF8Cm5/SrlyzaLQ5pAYGbzHEvxe2WdDbvNHK5KWmCQH4TGOnzjrhtha6oDjx2mFx/eIfgbcnTl+fOprca1oMg0R8L7//+y9aaxuaXYW9qw9fsOZ77lT1e3qwS4PbQOyhYyJowRwiG1EYkUBCSeRHGKnE8UBgqIEO5HiKAEFKygEBDHpgLGJjI1DiOw4EGhbgBWCO7TB2N146HZXdw237nTm8017evPjXc9ae59T1X277j23+jbfkkqn7jfs793v3vt91/Cs5+mynm4WD5cAye2hd0iRupD67z+pfa7q0tMJyC7bBwA8BPCXReS3APgFxHL5zV5F600RufFWXxaRDwH4EADkG7tXNMS1rW1tT8veDX6YDMDXA/hDIYSPisifwRcQGoUQPgzgwwCwsfeeMDrqYvKTADrNtVTajbvcSTA+GEIX+yRKtUrSdqkn2sYEdrF3qBDbiSlqXk8T3+0rB50xudxeQMl2KZAdaZxuna4dFjeGFJTN2EmijLzo2HM4pp90pOMYuwrgWxlzLObddA786n+G3hB34FZ91fLY0cI/+PF/Kf7mMsNr53GBv/bemDb7lju/gp/LvxwA8Pr9+F44Ve9vltgu+v88+jIAwPgzue36aUyjYXk9nvfpYgv/6Cuit9KpW9l0KT7x8x8AAGx8VucFQK7nlc+HEXxbis1b2+sTylbDDup6mtjcm9qC7uDFeWclbMu/nHeWUGaOL2m9m9rK9iTkmnfuTehr9UQwZke5JoMJb5DO70lD5vYKFbQuE2MQYD/WIH9kAEctTsz8JuG1lhbu1dDxYf4tl4EU85PYU3KIviB7HcDrIYSP6r//OuKic19EbgOA/n3wLoxtbWtb21O2Z+7JhBDuichrIvKVIYRfA/DNiIRY/wzAdyIqIXwngJ98jIPFLuSTxvqBVntDxb/Jg9a8CnocSd1dIpCKpFIX89mEpYtRPxLMlaOz3zBw2zixKlRSM5PPH/By9tLG2Fn1iTvO6Kj1GLufR0GM9+lROezfQYSsrFSbzgVTRDS6lZUnD1ur4vSrUtw12RqQHLJ8Lsj0GPiFiJALewFf88F7AIBfPIiKkKfNCLWWe8OS0H7v1WEO5P7Zpv52v8qls00vajtAZvEfrxzvAQDOZiOUR3qNNUruyoBsNgRJ0ivKZsGrbYrLTNpgc2nVmh6lqXHTLN1z5Jx2qc7HRoLxQ4Xi7xBcF2zs9dg9HtpFojHAr21fmRKI+ZdsNuxTaqapQRZYAcvPWsuMWj6NcIjN1I7L7nskkY4V8Nzk8lpuXjVL7yvT1+6uPifTNxF5L4CXQwg/IyJjxPL22RP87h8C8KMiUiDy1fxBRK/qJ0TkuwC8CuD3f95xdbH0245SS1xxcs1FLcRuGtNYGiXeBMmQ5KQzEidnlovHSJcdtj6jfUwb5FPtkJ9Ts8abIq3B8IJ721/A2LtUbWfW4Fcq7UF+1tgDQ2xG322lK83jTU4rU0swPlo2Sobg7rDeMavN1EI+Lr7FebBkN8OJaoP4DTiSV/820w4/+5mYhJ2ojtKnz/dx7zORGGv6Ge0nOvIkIh/+2VcrXuZuZ3NVK5J49p74d/trD9Bq0vZ3vPApAMAnTm7jla+L78uvxsWu3vRFhsc3dr4SyCNxoZFjVdPEHv4+X3Ef+xQHDHvP4AlLlqRbuwYWOk8STN+kuKCGP0ob0pWphdD8/ORB46RS5GOmZO84sYS1NzQG4/Ptxx6T+/E3FteHaOPyqLGNjOR2zTgbQC2AqGDQjdjPd4G9b5JefYMkTUT+fcRE6x6ALwNwB8BfQPRA3pGFEH4RwG99i7fe8THXtra1fXHa43gy3wPgGwB8FABCCJ98u8rPs7aQClY7mfLcxte4WnMXKA9rA081mmRrC+mBtuIX59cz5HMiVpWaUZNzkT846/1/TJoxacuk5vhhheXesBzap9KsNi4X7IiqnNynV5RbQpE9Q0tK7vaoOVeb7FvJbKdmyGWlz2Uw9906onuKjxxjPREnnLrAowtE6sv4YvxTHKZYFtrj8jB6FZ94cRPZCbuS9ZwLB9uZplEVxz27nVgvlIVq74lfnBYVPvvZ6wCAv3E37kXf9Jt/HSdb8TcfvazJ+nmGVUXFSx1rJTZmvmYnE4DVrlN3AjEhT+8303E3PYBG4k368dxnYTBv8fOC2QvFYI6qzXiQfN5TTSCCe6P0rvfcqT4BqpXGYyyv6TVu/X3e320OZKthUpd9ZMkkseu82HflSXq/7Ihvy9JpKNhrp2NdbaXm1T6pPU7UtVIsCwBAFQu+KBok17a2tX3x2+N4Mn9fRP4LREDe7wbwHwH4P692WI9n0gaMjho0kwTCcqiqBboCYm59IeTTSBrvYWESrx25qDl1gxzIJKgUaFb0knKlJuNItdiWqbcfJEwixn8v9nOMjggX9/h7rB6MUz86+IplZFI1jh/WaDbo+cTjlvfOcfaVsdzLHY1jmN/MrbTL99rSS5PVFvulwiXycgN2IXifjXpF2QJozi94ZYsU+WzYfW35sc7Lpmmm4z4KKCPeDvOb6vFosvy1f3YLxZnma3biWP/BJ14GNJE8/azC+m90mL4xHK9pRLXuvTEpGxKHB6Q90ORFMfvJQ86V3xPu6QWbP3pqxZkDJzmOQX5HnZys53GyzcJ1wjyhz+O2KTvua8shGpH8UWv3pwH5WACY+TGMEGzWee+SvpeftdbWAm3yZivI6LCxXNKT2uMsMt+LKPD2ywD+AwB/E7Hv6F03aToUh0vUG87B22XO2QvEh5wZc/YHZcvWBO7rLS1tBL8JmABeqqtZnLbABWKjIM6CZjKnXUCuDH2LF+JVS1VuI9vIrL+FlQJpgyXoLOS6tzQGeQnsa9Ibpu6A2JFvfMHnX77t2CBN3pFneCJihF3sKwqpM8GNtWdntZuh01U3bYehQ5t7T1R5zGQpUO3E17Z+I37u0a0O5UH8/1UsCKE4dTQzH26iepMLqFkAKO5p6LPZYXSoCzGrYr+Ro9phgjZ+Xjqg0pCImBseN6k9RMu1wDJ+sDQSMYaW0gWvJskQExPE8StNLxTlYmTSuJuZM9b1ZIx5zMn9OAAqJKDzSlCu19OwOceVsTayJNeMU+Q6DhYvsllj/MNSDzcSiC92VN5oywSphcJE+SYYv34Eq25GAAAgAElEQVSm49y0+YjzFyzh/6T2WOJuAP4X/W9ta1vb2r4ge5zq0lvRb54A+BiAPx5COLiKgT2OdUWC+YsT6z4FvEPW2f8Te5/hUjNO0YyjpyE9lQDpfQdwnaSk6jA6GLbfQwDRsCRVz6DeSNFM9Li6a6yujfz43RDJmc8608Cht7W8URqehjQUy2txS+nThiY9qgcrPyvzPFnsyx5fcF+21HAbpJeYd1hoOMgksnlnQZBeSH4i9MITluabxPAro0fqtZzFv+Vph/n1+Ftnh5FSYO9Bg4UmNidvxs+tlJSqPEix+ZqGuxPiQpy2gJnE7U8KFlqCmN/SHX6ueKPD1rwJJjyr7dw8iFHP87Dkp6kx6PfEe8ss5O7ReZh8cOWFBxPpS4h/QGz/BtCoNywhWHjFMHV0V+vtWTLo2OdnLmGn8sTK5O75cPzB3qOIYTNJjHKCls1bnH/5NgBgfH/Yjb24kWF0+Bbu5juwxwmX/hZii9lf1X//Af17CuCHAfxrT2Uka1vb2r4k7XEWmW8KIXxT79+/LCL/IITwTSLy71zVwB7HYpK0i2Tc3HkZd+pu044EhYLVyoeqo3R7bJ5AXx7WOqiN3EfzFPMazabuCOPU3hupLGyjeaDRg5X1+ViSWX87ZK5tRK8lWzjRU5+hnslrkmalfUVGbuY1d+7OdaKUptM0lCaunZOavo57PuM34+652h9jdEzdJU1mnjprPXe05S4RrmKlYpZ7s+PUErmjI3oyRLp6/5gseIzGdvFS+Vum9+P3Dr7Gu9mtP2wZjHaTeZdmJKg3mfWEjptgPMHknutPcT5MOZTUljNPftKDNFrSGp5c7WtJBeZkNEE8TtDqfVHrPUPScAkO7iSqlkl+ACj0HqLiZL1VeJJ8cfne6ZeaeZ9k+pd0o800NYJ83sPFaWvlb1pStaZ4utqleqcmv+9VyM4rPA17nPTxhoj8Nv5DRL4BgDKw4un4U2tb29q+ZO1xPJnvBvBDIrKBuA+cAvhuEZkC+O+ucnCPY0FijsH7PIZ6RCFJepSVbD/1KgDfyxads6Xp6s8cwOrayD0lXenHByssb6iEinpFyxslpq+qdMXmRMdDtrUGS5XjoMB7M0rRF2aP43VSaoKxpncVop6JeS2zF0o9VmOAMcqrkO2smRQmsu6elXONsLLWJxcfP2LFSXlacq+EZEuW3sU9h3NWnPw1ct3kcweYse0j2dUdvktQnigb4L6yvT1QVsMkM73rTOnS+r1O+Uw9nq9rkZ3Gz239BlnntDq2CkPIAuK1c4VEuFkHPHNUhCHArtn0Lr2GlXmfJomz6iznZVSbmkfLz1sjtZ8pT8zk7tJyH7zupiM+7yyHSImYIGLeVnGiKqdliobMiauhNlRSBQNyjlTKZXFrZMelJ91s5F4VI1F+ryWl2nvHtFEDe5zq0j8C8JtEZDv+djjuvf0TT2UU79CkC8jmLRY3C1MiYF/J+GG8GPl5YzcFy3Ojh5VdEBOf790ohmxV91nacEmUvRulGL8Zn4BmI95QQQTn742JTS4kxr+aJ9b4xvEUJ5ULvhEpup36OA8v3jwdwF6nA2pJ5S5TS5oJPWa67OwGtFJ561QTtKTq7PxYBud8Tu7WOH8pLpjWiFf3ULpjnzNSIFxsHE2XzqzfKRZmtevUluMDIlY9sd3yf20x67DYjz+w3Ce2BMjm3Bnin8kD9gL5okde3Bh66VxSxrgJCKn3JQG+2EgbrD+IErBMxsd56HSuVqh2htSthEPkJyvMX5zYHAFAtV1YCDe6H++haleleo9WlsglDWeXJvZb8xcVGrEMGN2LG9r8PZR20KkoxOAXTBskVbDwamT4rqxHjKWh/C5pXTPvl3pCe5zqUgng30Tk3s3I7xtC+G+eygjWtra1fUnb44RLP4lYsv4FAKvP89lnaiFRKdLgZefcko2a0NpJUaisKPVy5rdHhool8C6bN2g24ipOJCy/h0x6xEPaN9NTKyC4DuI7GMMahhpIxHdK9RpW18pL9BLlcW2gMLquVMUMhafQ6m0mol0tMLfQiN5IZV227PzuysQShfSoQtpToaSTo17A4tbIkuPmveyn5jkQlDc6AFpFjVZbjEXUaxgl1oEcFgwL3bM0asuetG+9NfRQukwMDJgr9cT4gaDe1Pe1invygSFNKgBkU3ZBe0ma17bLEwt7LuoM5bPG6BF4nbp+iVmv0/l7p5eIw5w8rTQQKPWzVvuFIX5XqkzBpP3q+ugSiVbsplfPq9dPtLw1HYyNyd62TI20jWHh6MHCihcrfW/8oPJ7kYBV9lSNErtfn9QeZ5G5E0JY6yytbW1re0f2OIvM/ysivymE8MtXPpov0EIiaCYJsnnX2xW1NL30squVDkeaq6iCeSvG7bJTWIlvfEEiI1s45SJj43QVzIMxYfeqs/yJkYaz/+S4NvkVEl9JGwz2T6t2i4FGEuBJufJgaTkk7vBJHWxnohflOsiFgf36xEwtvbFeqd5i95q0pFqSHSeXuryTFhgpqdVKPZn5extc/4dMiMbfkt6ObB5bqWC4g4CFzkdrc6Vl8Y2ASoF909fja7ObialKNhPv7epuxmTx9Y+oRvQ2+6uC9YBZ1/RULGns+kipKyqSMvPM1TmXN9QrPBuCFOM/2ILhfUEG1Nt18jQen+0iXSoolkMNapafAfd++x5WuztsjQnJZf6bIOy38HvMPNLbY+8lU1tdy+27Bjylx5YH84ae1B7nKP8igH9XRF5BDJcEQAgh/OanMoInsJj4jSx3TNYVy2GvzuiwuSTsnq46FLq4METKzxqkrJ5MnPEMiA9eozcIKznVricpeeGTKjjREHE4C1/ETGir8Yy+LVA1m9taX9yYoNVFZ35ngtHDavBaM0oh5MGdsNfJb7BKk4iOOwmWxCRhV3HWGpctb2xjqSvEqlx8rdoBVntagdkktDnF4rqen/Y9kcWv2kz8gdD5m9/wBHemQfj57fjb1a2qh6fhNRF0unilitGprjcQ5RGe3yYyV+elFKteNT1CM9J4JA2R0L35UuP8VNuZLS6c2+KksY3MCgVNQL3n9xvgFZ/ljdLOk2FwH9vEhYr3XEiB1BZ8feBLQXk4RK13uVhynsYFK607qzSScW/jtYWJBppsbuGbi8kZTx3tbmmAJ7THWWS+7an80trWtrZ/Lu1xStifBQAlqno6hfOnZNJ0KO/PUV0fo9ReDe4QRG1G0irtVg3umdhupB5BtZ0b0tKVDNg3U1vJWFr1WtpgOxOtHSWOEOUmN1UsTR2sS9a8nDZ4ufwgom/rXXdry0exvLm4reLspw0q42Bt7Li1nutIMS7EdvR5hVmObCYpUNPL0o8FuJdHrywj7qNBor1F57c1Mb4j2P51TRjukvLTQyiOf3TUo73gVGXxvY3XPfS7943R29r8jH7v1QKFqnzRG2rG7qWMHzLRmVkIVSnyd/vTcfzLncRVRRVRnFady80ymT3YrMXmGXBKTMB7l0Iq1h/HuQI8nOL1ZIf06KAyb8E86uCeQ0iSwbG6QlA+iq6dJfpDamF4PzlNr8a6+lmcSHvj1pAxOa+Qj+ndqNf1qLqERjaPSiT2XT0F+7yIXxH510XkkwBeAfD3ESVk/9bT+fm1rW1tX+r2OOHSfwvgGwH8TAjh60TkdwL4jqsd1mOaCEIeS4pEUJoIe6/kzGSmUWfWHRr9PEuH9UaGENirdEF3qUy9nKyJw66nE+zM8657TEsfKNjq2sgUFZhQy08bJBqvr647QpiewPJmrAnbrtt5nM6YP6QJssUwn8IEsLTBxs0SZXHa+G671cv9cEc3wJ1v8UweTx5o/J5mBogj1WbexLwJAEzva75L8xjj+w5+hILx2lGKudKX3vgF1ce+w/F4T9TsThzH7ie81G1k561Yv1S9MfQqN19vcPZinOfdT2nP2n5hpWV2oq/2C59T0lMSKVwF+9xSFUTH9yu7P8B7oA3We0QrlJB7fqtEecw8jSfmDeLQkDPGe6kIT3BumoDslJ60uxf5qRKJ3xoyCoQyce+GygQvbtj7/LvcL4w4nN4NrcvFxv2k9jiLTB1COBCRRESSEMLfFZEfeCq//qSWCNppjmzeDtrugSFHrTXIMYQSsUUiET4sHfJmuECQ8Gd5vbAQyhskEwsBbKGaZHbTGPRcKSXSeYNqR5vQOlbAWqwUmdkXRoeKnheHwyR2O06sqkRUaLpsDfFLKghzvXNxqZVDPvipVxSWftPlxvjHahTDlMzCGpf7dXQvMStn7+tw6x9qVUsfUlYs6q3M6ROmFDUrjEOZlb69X41hwvHLJZbX4m9tfNYXTl5TU59Yib228bqOl0nepWDrteEDlJ+3hm1i9Sefd/bwcyG00ChLLMRmFXLZW5S4AIWRWDjKcGzy2ZnOe2JoZ0L8Id4ewoWY4XiyagxBzs1mtZtDtFxpVawUaAsmcoehTjZr/frZa41Vt7iRWHMxnC3Pz82LKU9qj7PIHGvf0s8hypg8wLoxcm1rW9tj2uMsMt8OYAHgjwL4twFsA/iiaCno0thrI52T7nD1pcu72s08kXrsukrF2QUmptZ7Z7h7GW7m3gpdwZKq7lij1Kkgll72M7fzQrNlUidOgETdo0mGTJHH1n8UPJG32td+FnVpu8KF3JgAXNwozK3tozVphhUhrea87SX3OMbEzotmTXSjxIipuMMn0wTlkX5Xd8DtX3fu4Pl1LZueOJcwj5G8oWL20pkHQ+/j+OV4vuVxh4034ncPPsheGjEMjMnbVo4JssSrJbBbo1/gvMyvZ9j69ELHrQn5VWfejSFtqawwFjQYImfH95ZGBNX2sC2iiXgm2qFI4S4VQ5dXGga148SOx2sn6gHPXpr0aGD177LrIZXJSZ2aJ2X9XVr8aKaZnTs/jx7Fh3nD09S8TXq1LGGPH1VGlvak9jjVJeoHdgB+RERSROKqH30qI1jb2tb2JW1vu8iIyBai5tKLAH4KwEf03/8ZgF/EF8EiI12MW7NFY6U4JkmNuPq0MTQlV/687oykx7qqC4+/mdOgB1Fv5uZBrKbMobTo8sy+CwAITiVgWj51jwxadzQmoEMrNt7VtdLGaHkRHQ/PLfZGqeejROhdMXG0brhAr7hwr6WzJLaXdi0hPkqx0l22PBl6NJuvzNBOhrQEae25mJZJxD1BdUqPLr4205J3tgxI1HtL3x+Zs9tfnnr5/nx4zaqtxAiy2K+EJJaxAWC567khbs9GUlb2EvJ6KnNF7Urr+S3TqErFPEuDM2w5QNLUBHSuqp3CrgHnQ9qAxU31jCwnWPq4uuF8hx69Jn8rJXHWeee0GLWfG71IQ2lngjAewjXo1ZUHK0gYPg/Vdo/Wgc525xAKI7nK2BOX2W8+qX2uEvb/CuArEVUKvhvA30GUjv32EMK3P8mPisgfFZFPiMjHReTHRGQkIu8XkY+KyCdF5K+phO3a1ra259w+V7j0gRDCbwIAEfmLAB4BeOkJNbAhIi8C+MMAPhhCWIjITyCGX78HwJ8OIfy4iPwFRBmWH/ycxwoxR9KPHblyc1da9eD/1pezWfRUGl3PhuW8RFsBOuoVnzqgKldg13J/1Ov30L+rFiFjhefCbhdcJ5utAc3Y+2a8Z0QwvzUkw7LKTNW5GqZ6KOVhbdwhBtPvdc86bFx3ucPL4LB02Q7pJeG5hdl7JpajMG3kELDadYmQ+KbrZ5MfZvxqzJO1ZWI5smoZ52fj9coJm9T7YD4gCLDa1iqQyqvs/npnfUdGsJQAE9XNWu6xoqVztQrmMVJbC3DdaGprZfPWtMRJQnaR3AmAtW4EEQM7Wk/ZscMCWO1yTasOTJrQgykPVga54D5PbyRddiZ/wvHns8bGyDYYab2Fhcclo8Di1si8cUrAILjXRNrYtM8jpOdKQCrqYGDUJ7XPtchYZjSE0IrIK0+6wFz43bGI1AAmAN4E8LsA/Fv6/o8A+K/xeRaZIBHLkp82zuJurPwaLh03drFS8uIuWkcBE6jZY5Vn+70xvu8VjiPpNf3xhiKeJl22PRqA+IcJ5maUWmhmD9cocTE4/XxSB38t7YUFiDediYJZc1xPMJ69Ufb9pMe4xlAkt0XRcBVZgvIgYkmWNxSbo1c/W3a2API8y6MGq51k8Llm7ONkIndpDZCCggnunM2qhSWNOVcsTZ/dyUxgLWm9QdIoDTRUo9BenOdhqDigjdjwJKj1FukCPrtVWAOo9YWB5yQ2f2T5K48bK/dbOXwnsw1n+pn4iNR7Y30vH4TkQOTz5T3J8CpnKJOLXR9e47ZILDTqaz21Fzl72Ry86mwjKckhnHifHBHBSdvZxmFhpk5btZW6jtMT2udaZH6LiCi4G4K4KJzCGyS33v6rb28hhDdE5E8BeBWxavV3ELlqjkMI3HJeR8wFXTIR+RCADwFAOdp5J0NY29rW9gztbReZEMLT8ZUumIjsIpbF3w/gGMD/hrduwnzLFtAQwocBfBgANrcVDpoIpGJSSxNjJLE6r5C0pCpwCkXuuv0OWZYmTadnr7DP0+iFJMsOXBK5Q1U7ubmpDGuyXrL3omeSLTpP2rF7OwRLgFrSlgz4Dyvr2rXxbPpv0jMh/WY/qcn5KE5rAwry+NNXTtBsD9vSzBPoesfV3a7ayjF52A7m6uCrc+x8evjaRf0gAEhSdfFTD0tI+UD3f3TUYbkz/O7osOt5Fd53ZEnYxMNSIIYQ1qXMTbp3SM7H6LBxD0Ovz/ymqkyOxTiM5zc0rMkz86Dm+x6iMUm7eGHDxgbEwgOvsQM6MytP896y4kHnHofNWdXZ/V1vKo/z4Qpd3gOGwgGgSe1UmyyzB/FUQlKR5iKz0nnKcDB1cOrTss/bu3QF9q8AeCWE8DCEUAP4GwD+BQA7IsLZvQPg7rswtrWtbW1P2Z4OK80XZq8C+EYRmSCGS9+MqEb5dwH8PgA/DuA7EWk/P6dFHeMW9TRDl11kmtfk5tQ1qI3rpfN8CoFvSReQkYx8+0Jhq8MlwJZ0wRqLwWNUne+a6dCjShedtWbnyjgvbecKkyS76n33YjIxZGJdu31PicAr7orcxWICkF6R9m11fsnZud7sjK2kb60AuptKFy7txCFNsVQ1AeoplSfhElESx12cuZpjSk9GxJLy/ptOpkWVgrOXdB5TwehIS+7q0Sx3HZxooEbtK5q96J5Zv82BYzT1xW3Pt1lrinpu5Wm4pLoowXlhmODOZ633M7H9niTq8wb1ZszP8J7sq5WyFyknedk4A5phm4B0AfmRTojmeqrt4hJdqGtyBTsXm/dF693jK7+XCQKlXhg9m2bDCeqf1J75IhNC+KiI/HUA/xixPeGfIIY//xeAHxeRP66v/aXPe6xUUG3lKA8ry6L7RHoyNGjjoMmmJKm18zMzH0QuSZzk2rsUssSqKAMuVybJNnmj9PAaI4ZrLsolFxjS+mPqu/TWE5Mlg/fOXyyNtsBIqELvGGx81ApENu+88Y4hRhM8PKAsTJ6gOHa0aDzPeLDRw6U14BG/04xcTI/J22bsDw4VI5LWP8ME4/uvRYW2o8mmh37sI+vd02TXy+f8GyyhnC+I8vWFjVWj6T0mh7vBQwdokpULyjVXF2CileoTtYaObZmYYgAX6Wx+eSNBF6xaVJyyMqU4rMwT1oYLmrWXQvjldV8UeQy7h05rNDsjHQeVFHo0FTqPJDvLzxqMVLWBdBF9WhJj3huntiiavIupG3RG3vWk9m54MgghfD+A77/w8qcBfMO7MJy1rW1tV2jvyiLz1Ex38bb0zuKkH7ogYilEm1+ZQG22MiyJzKX7fOrJYOvt0DJ3cVihHRWDYwRJbZezknMiPUqIYWk6XXVeOu7YJ5INaBziB8XQo/Q++Dsbb6yw3CsGn896mlDcjcxjGic2DoYk1VZmST0eKwq+Db0bhjpn75tY/xN3tvK0xUIRufXEk9nNNLH/BzDA3jAU+sRvxKLhe2ct5vvxeNRKWl5z2lNiYqTTsvlph9VWMjhu1ju+cRjreTTjxErixD+tdnObD3oB0nQWbrbUzzKuKDFPY3Iv3kTVdn6JfjNpXbzOkOfqvSz2cysFG63DNIW07C4fhvdtmVhxgfdVn46C92Z5sMRqL94n9GDovbdlgupGPJdBr5N6vAwpzQOGh02Gw2m6tym9fOH2biR+17a2tf1zZM+3J6PWTFLXraGY/dzzB05VGT8/fnNp5WlKuq6ulQZcmt+OOwR3x2qn8Ng8uLogdx8DAoonX4mu5C66vFbY7jXQWmKCWnMnbSGXwHhGGbkKdjz2Y1XbmeUljHJUxxBSuZSAlhY95LEickep5X+yM3oVTIK6V0Pi8ZCI5UwIlguJH9coP3WXPntPaWXf8XYE/VWbGxhpnxR3YvYwtbmYR8rO6/LEEcckA8/nPo/sZiZgMJ85/8xSk6Cjw9r7r6hgMU7N8zOpYOF1Dd6RTZWIAMtjmJe4aKz7n/NNzyatvMjQgUUA57Wx8Z77+PvKEgCQn7aWRzMSquuO6iXXDT2leiMzLy7vqWGQTpQEWyERB5JSinhGTa7kEgHbO7XnepGRQCWBBNlpfGB48RJNnhZdMPyAVRnGCcZvRNhoyJ0nlck73szEcbSj1FCS9iAf+8UzJr3dwol+NEwyfeqjesDxCgDjN84wf++W/T4AjM4aZGdKW6Fa1WHJSllq52K/vfSqFasupv3dBmt8ozYzpLd4akVpdH+B+R2Vou1VhAAN9yxpqKjWV05x+lURCMmbM0iKfDaEw/MBLWadoWnryqkTiFAemf62S9bMbsaBbL7qLRJsIWCic7WbXWID7DMdmkyOzndbJLa4FEfKljeeOjduQnR0a5/n4s/rOb63RKMNo/0w1WRPqImthQXpfGHo44Ycnev0IzZ+Xh99rZmmnqztCfyFCzIs3BiKk8auGRUKpAt2XBP1611bngs3wJC4PvqT2jpcWtva1nal9lx7MkFi2FAcN6iuxTIrE7MMh+D9aYNS4urGxI4BRDZ/SpL26HsBxF3A2vRTbVAcZ7ajGnHSUWUIy/ysVyZEDHn6iTYAmL+0Zcz0LJ83SFFtTwefY0IvXbS2AxpOZh4ALYemleJMcpZbW0vkTV6Pntvq+sRwOizjrq6PnKG/GO47SRNQbw5fW9zZ7NEFMNwDljsa9rC3xxQPWutjalbxd4pzJ2LiNTN0culIW/MyMu/p4a5bnLX23X5CPo4rGAQh1wtab2TmpSxvxusvbUBxHK+BUZoSLVsUVtYmnqkdZ+Z91L0mSGtw7PH4xvEHb+DVc8ln7WWMi1pbpr0+Ng9h7L6qXHjOvB8NtUZG65BFLmcAibZjdb0QtO0hnNkQSXS0e4ZPj/xy7cmsbW1ru1J7rj0ZSIxLk1VrVALckdnjkZ83yA/iLr54aTt+rQsW4xoFwWZu+QUmeRm7ZrPGEn9GA7CbO/k30zBdMM+FZUL2tIwetrZLNNoVXD6q0F3IsUgXUJy6GHwch+Y4giDVRF5GdysR+03miQxZWiSe/NSdOyRiXh6J0rs8cRqCQ9X8EfcI6QEyp9VlDmrjrtuWguzRsJvZerq2M6ebnKoXtTU1XSZ6ZUwYVxu+953fptcSXE8K/tvMHeWnTjDG8TPnxNxCft5e8tQgwPyFsc4HgXckGW/MuyFReJcKastluT5S1wxBbfTiykdL5KfD3qgQW4wBAHOFK0ze1ALEXm7f3fxU7E+u98Z2P/NaFCe1ASF5b9JLS5rguk55zzMx9C91pVLLIfIamMe7P7bn4Ult7cmsbW1ru1J7rj0ZaQJG9xfG/wL0PJgzjzHbO7GCw9i72i48xj31aoTFqhfE50MmDvLqeUwGoNNdY3WtxPhu3AmazbhDWYkwdFauZvzbTDOL5RlDd3liu4u1N1AAPgSDn9MTy89qNJMe1SOGVQwTaO9B4bMLhFpI0OO6UUImek8rHzcrOM1ITPuIUP+k7dNnqgZWj++E793YjvSbYTHB7JbuwOTIIffNwqtLtGwZ0Kojw/aDbOVtE9KynYS5sAQt+8ZMq/wyabh0MJ1py/mwC7pzbyzVqlU3Sr19Qnvc8rPa8iLMPVkZepy5jlNPbsTldNhCop7PQWWk9fOXNu17F0nim0mK0YF6jKm/BgxL5Dyn5bWelIseK121PeBk/Ev9ryB4ppIoX7QWEkGzGcmPqFtDd5Xo1Gzeos34mmJjDleodnUR6JecL+TiiN8ojzpLkPlNnDomRhe2thB0pbq1mhzMD2PzTbU/daIpeJKQN01fXOuiZKgrHniYR9e72cidSY09WlbuFDsHNhB2RYKZissx1GhG3lcVLtAzJG3AanPYz5TWwRC/o2M+wMFK6MLxKn6nywWVonW/+eanAAB/T27YYjF9UyWGGcqMEkzvx7Gdv6CJ8y3B9ivxc8s9znFA2vEp0XBlrGNoAvJ6yFFbb2XeP6S9Wu04s7ms9dq5uFvnpGbExiz9AbbjbuaevD4njaH3hRFPY82IvZAtVYwL0bvSBSe5Inp83hruykwcjcyFZKRCgl2Z+eKo9+sqE1tMDdPTV1tgw2bH/roE5aMhidc7tXW4tLa1re1K7bn2ZJDE3du0ZQB37en+TzPbZcb3IwCr3iqwUqoC0jF246TXrWvbV3wvddpLI/5pOws3GOqsdnNLtLEcbgRSmbgnwzCrh+6kFceVlbNZ0mWYN39h7N3jxtObmCfTXdDmATKkF/q2itMa6WIY+g1KsEuSI/kuZ/Rl4onLjTdZIo2fP3+xvKTGkPeQx0kTD/L37r0cjz9JLPFrapsaejUjnxfScHaZe3YkrQqZgIQbpi55ThRxZkl6Ssxm8875j1kaz73Dnt4N6TrrTQf00dvJZjVWO+xK15D1tLZSu3H3amJX4KHnaqTeShtcsbHyUnecC1eTsGuxkVrIzPu73kjd6xgNPbBs1tp3LYSqOpNHFt4eSS+EM8QvvZ3xJfKsd2prT2Zta1vbldrz7W1Xz7YAACAASURBVMmoSd1dUnj0Nz1hyXJxP6a2Lull5z1I+jYTpOh68G9NajZp6mRSJG0+8laDi7SaXSq2e44ekrekNO6QhSoOdEV/hxpCw/Pz1nYy6/aed/b/3BUtvu/pQHHHWu3m7ukwGZyIlY+Z13Gof25JQZ5vUgfMryvz/bF7BswDcBz09JqJ784ni3gue0et5blWVF7QXqbFrhgXDRUQdj9V9fq7vJWAHgbzB9x9kzo4QTl1vqvOvsver3bkeluWs9M8Sb2V2nd5T3R5eglw145Su3d4n7CtZXmjNA+673FYT5ni9Przblwz5PZpcanHLeZuevcWnIem2skH+kxAzL/x3PvgxxXzcyzfT7WIIpeBme/UnutFJiQSJz3k5pMl1nSnLvKstcQY2cJCIkbxYNiFTCAXsu/Wtt8EYwzzB7pDshgeoy0Tf6gt6ekUCnxYm02lWGg6o1vot+mP7+oitOPuNaCEVmSyzzzBSTeYNx3Rmm2Zopg7QhSICWuGJ4bzycRu7BbDUCr2OqlLT4+9coUEuvGjQ5fhJXufIaFPWxNrKzJXC1jsMKk77A8qZgGrTTZG+oPEB9EoEHZSjA6VuoFjZKgRugGylfMt2tBJXEh5XFtS1xYSLpZ1QH7KBlplMMwTLxD0GBcXSq0weZOqD/HaTV85xepGRHCzoplWHaQdVv2Spic5eyFcComHXP3ihKFz58N7XloAGrW5soJYqFjlzgE9VkwTE8u8R6UZcls/ia3DpbWtbW1Xas+1J5M0AaPDGl2eoAM7lVVbRnfV/Kz1FZ6Ix2VrHkmfWIlJTIql5dbK317qEwFgglu1hlAhFXM7yQjPxG+b+HpuMqhVcJTxuXsfzcaQY5gubT7rsCSFxJFTPlyUnV1oonP8sL6EBg2JC6/36T0ZrnH8tvtLsPIzvb/VZmo7amMoYPH5rYbJxEggpWPM4/HP7mRW/u4y9fpIKTERbL7hnwOAxfXMQz5N2mfLnodJKgviX2pPzLvEa2JJ3ZGGV8vrhZdq2bncQ9cur2tPXOHhDcMShjPZvDGvLaRDb2txZ9PVJNTzacaJo5t3hvieZNUaUpneJ+cX8MR2WneGAyJcgp7b6N7cvtdOFbM0yew+2vh0xCrVuyMLG+l509vpMqcJeVJbezJrW9vartSea08mJCpkP2sAojuJ0u2pFZAEeuOzEY07vzNxLhMCn3rfsYQewW2LdtCfBMSydrM5FDWXXvKzL3Bv3+uhTIEhJafJ2XbB6Fus7EzQ3DhxRQC14rQ2cBXLzuOHcaeVNuAiwDCpO/NkOvWuulQsdidgjDkFaYHxI31N+V4mDxsr0TLBXW0LJvc1wbr0RCsQu7OJEF5UynmycDF764PS+Z++2XhXtc779G6F2W2Kwfs8jqhAoR4NMke/kjvGSM8zMbgBZVml8XM13aceCI2f4/fywwWWtzd0HLzGI/N6Te5Vx50u/Rowf9UVXjbnXBkNZ5agOGYfFj3ezuR3jWZ21V6mYtVjrG6MDUZAdLk0ARuvKjBUvTNpPbfGe94SxoVz5DypPd+LjAjaUYJqs7TKRNDQwcKPsctt9sOQ6WtxwuleL2+MfBG4MLlJ3WL0aAiZD6nYQ5SaxGjSY/RPB+9JGy7pTUN66gD87eChCqkK+klshjhcWDJ42MMQionM1bXcBcU0rGlGcknmI6mDLS6L21pd0PGUhxUWNymXou7+JLGFavyIDY899GjvQQCAfOENkktdZKYLv8HT1cXEq6NTy4k3lfKmL1XXtDxuLJnPMI/EV8WxhzB99KuLqMU/+bLBKh+GwrawHFU2JqtK3dmw+4O8v8t9lyfhMez6zHxBrnaY8A+m6MBFIH8Y78fVrSlAFQndoMZ3Z2in5Jh2/BUXQ+MozjwwMYR3T+xwtav0FqxyHlRoC1KX6Hj0nsPbUFG8E1uHS2tb29qu1J5rTwaIu+HkvuNTbGc79gQtdYzoUnepYKHt/aMH2q6fuG4QqR4MtTvyaSJpVLJorSRpWjgBaPKhB2M7Z/BwwDAR9+bo6Lr2vCcmpZfXoxs/vhfLos1mPkAcA9GzYlKXxvMsD3qcttScGrtmEnfd1U7uWkwsI7NMfC23JKXRTea+y1lpvACy1RCb0UcuXxQ9TlcdGiN6opfIBlX0xMm8fG5EUARkp2LJYF4Lw7WkgsC2Js6t9KhJ1btY7rmaAK93v4+HCVxLrk6ynr5U7/GhPK41k+r87eWG03KVAx8nPa+QbugYnZzLEsUbhXmzTEAXJ4336W0Mm3273EOd/vNQXKDDqLZyuy7s/aNnHRJc9rzfoa09mbWtbW1Xas+1JyMh7nDSBttVSJjkCbvaWuE77VrNFt5qX22zM7uyXZyk2wQmFceVKRhM7ml/y1ZmaF7uWosb5aU8gCXqEu+gZi5n/tLUdjkjOyodfetUkaUdy/qw9DzHb85R7cU8ykJLn4ZwzsUSxYy5Rwe17+Y61mzW9jrOh8qa+VlrO2WfZHylQLp+otOoGIphQjdbNFbu7ZSuc76fGVqYOzZtdNgYQZWpRp43RvXJ8+v3ebF/i7mnNkvMeyMRV71VXFLnTCu/LrwGtGaSGWhvdOCJUXZRJ735o4fb9cjEaNZ9zQR0mTpF6pIgSc2rPaqc4P2hK2lQVpceWz+h76BKL+MzaWxe12bqRQnCN3qemn1evb+kCX4uT2hX5smIyA+JyAMR+XjvtT0R+YiIfFL/7urrIiJ/VkQ+JSK/JCJff1XjWtva1vZs7So9mR8G8OcA/JXea98L4GdDCH9SRL5X//3HAHwbgJf1v98G4Af17+e0SAJdRQ0kZuSN/tJ7jchzwkpIUsmgFQCILQT0SAzOrZ+pdgvr1vYdove+AvXK47qnXOmlQCDuYrnKcCxfiFn+bNZayT3VHadtsx6RNGN/dpGvkC41j6Iw92bkuyK9EFo2C5cIsNJFYyLvrJRd9CTi59SL2i8sf2FeWi6WBzOQ2NJ1k5hrGR1QFzq3ubLyfHYZKmD5lSIxHaVOVSbrDQf0cXeudgqjwGTlpk/WbXpK2pOUn1ZG+sWqUXlc9yqGOm9z702yFgbVvuoy9z48z5SgHg97xNrMcyfMgRBKkS4cugBK8phuuKuhEmCYrjoHmdILnXtOkEZy95CI5WeY85ncq8zbt96lhSuHemuCt6g8LQ/kyhaZEMLPicj7Lrz87QB+h/7/jwD4e4iLzLcD+CshhADg50VkR0RuhxDe/Jy/IRKb07oeodLK0Z2AhgTUIyINQ49iwXR6JlmvIW2YxEtqx3RYUjA4562hX8eJufKWoGWyEsDyli8ugOr10F1lc979GZa3Yq+LiZ5RanTi1APGyXp/hZrj1HM38a5UzFfl+OvN3CkTiNsoncrCmP9ULaCfYOSxqo3UKSr0gainwOgw/n95MrxhIR72XNuMWKXitdLmb6KKACdfFucnLcSkhbng5vPOQ0p9MLsUaEdx0WDIwPkpHi5Q6ULsYXBpiwA/t9gvXA5Yw9T5LUU/n3lCfeM3TuI57U16AnH+my0JrybD0nS1k1nYMX7goTzPz5phWWZfNMhP4mZElrp02V4Kf6R1citTTzCaCW/23XhN1Rb2SodT6HtdJhZGjx9egGNk8tRwMs868XuTC4f+vaGvvwjgtd7nXtfXLpmIfEhEPiYiH6vr2ZUOdm1rW9uT2xdL4vetMkxvuYyGED4M4MMAsLl1JwCxJZ+7vRFJqeRsWxZeQmRSq+ocGcwO3FQsKUh1QTL8t6WYfg2ty8RQnpmqNLbjDCulhszp0mvSNFmFy9y6ACp2WmvZud0oLaa4GKaE1FUfeb7Vpod5pLtg6NilYqEf2LG7bHtoaPUk7i5w/pJ6WUt2oMcxju8uEJJCx6qvPayxvDYEESaVhw9WOman9nkL8jrMFIyX9fq8zt470fOM38tmLRY3NMTQMKSuEvOQ6CXWG5nRYfRLu0BEtV7kam4zsU7xTDvoNz91iuraRH/L9ZyAGMLwutT6mTjOYZhXbyRWBk/0nuC1m7yxwOIm+59IhdEhm1OdVMv8eo9WewWkHaos9HuI6Mk009RpRKzbPH4mP2mw2h/2v3W5oGBSXz32fBmQxzamS+qmxUmF5ClpLz1rT+a+iNwGAP37QF9/HcB7ep+7A+DuMx7b2ta2tiuwZ+3J/BSA7wTwJ/XvT/Ze/49F5McRE74nny8fA8Qdu9rJMHrkoDPmWFb7cYdIqs6TsDk9hF7OxnbbxjhB8tPothjLfSOXeoyK49pzFJp0DKm4+PmKO5omLqepgfz6XdPdhTxDyJJLv8V4PCTeLWtqlOI8K9zhywcRor68ObFxMCfTjFKnoIwOG05enmLzs/Ef1m2uJeFqb+R9NgqaCz2PoFClzLQG5vuadNfPjQ4ul6i3RlpO3uwRPV3Ik+TntYHIyCfTltLTeIoDmrx6inpvMniNc5UuWtcN6vUuMZFLk8ZJvwz4Rz3oVCy5KnMvkbOczNxXft5YwcEJ0XTcPfCeXYNJCmnpqekx1BMa3Z1j/t6Yk2NSe3mjdI+U425753pBmVS6YNePoM2kSlxVlYnlDJdyjcwpjR51ltN6UruyRUZEfgwxybsvIq8D+H7ExeUnROS7ALwK4Pfrx/8mgN8D4FMA5gD+4GP9hrKDReSnt8oDALjonNUm1WpIysaz6qYIkIklAOcvauhA/IHAyI4sgbqROZaEXLa9B94EzPngH9WWeDbGvQAg4YLjocBKbwZWZyyJvKzRTorhuHNxPMqcC2x0z0MitgAWGtKls9rwFMQIbbyxslChjykBVLSNuBuuFV3whj21tAoYHfqCEAcc/8xvFrZAdcFd/3rKyoZ+Tx+45f7IFoi+KgLxOuyhWtzZHOBAgL7cTGoVG7s3ml5TJsewN+mhdIeiftIGSELkbB9j5Qs2oIuTvuZ9bPqZLEN54NzS8XwdV+PidMrst11i8kZM1vK+zRYdiiP2SSl5Vikm5ueSxdqQer20JDZ7tMZ3zzF7X5RYYbOndMHoSvq/BcTq5UWU9ju1q6wufcfbvPXNb/HZAOB7rmosa1vb2t49+2JJ/L4jC4lE9y5Ej6Vv5qKWqeElyLtab+W9UmD8fFZ3l/pObNNNxUvkJCAKPbeamj89lv1k0drngCEJEN9rppkTZV1jJ3CL0UPlhB0zXFJPbJQiaYZlyOK0RqXvW6/Quf82sRMsz692c3ufSNR6K++heon0ZUd3b66MX9iVHWjVVDB+wN9Vb27koQB3xVvT2EL9utxE0N2eXothl0ZivT20pAkWttHSZWfkYNYXZh5Z3cMxZTa3xI2M2UF9vewJAQ51rvLz1vE9vK61C933aRLovZH6YkASZsoV6pXdX2B1fdjtTl7mkIijlnvI4nNNjpeK10pasfGSrnXG5P2sszJ4vaNo8Bc3nOKVNCQjpxKdKA0KaSD6c/qktu5dWtva1nal9lx7MtIG5GdDRT/u5t6zk9rOxLhdOs+tOPLTy3WkKUx7BFFWRiQ47Lh2ZnxSbY4S74B+FHcXlsEleM6ByWB0wY7HXUaCx93hwtWRJsqNAj29nEXbo+4cEmYhuFdj+Zq90t83VLJg85Nn8f0bYz0X14tq9dzPX1Av4LC7RDSVLYOrYWrykIoDCH499su4Y95bdpfyV0QZd3lm2x89iNV2YqTh7JuSLtg1Jaqb16zezJCfkrwrHqMZp5aYZ4d7uuzQbLBviwjl+F5x7N6QJUZ7xGGkeJUuuaT62C8721wyR3VnYvmTvnwwEPN7huo2VUlcIrLK5q15PMsb7n0A8T6jB9MnGze2AJ2PdBWse1zqy6qVXfp0kjJrT2Zta1vbldpz7ckAyiSXOZk3M/PUug6JmJdggupjB+9xd6m2MowUwEe6FO7SEnp9Jytm8AskLOlOvZRIr2r2vg39Te3UPakhqqLIsuLyWuEMcAfecUvvht8NvVxIn5ME0N28p/vd/3yXieVuVtdcXqUvsQJEfhj2U9HaXtmfOSeqRjY9tU3ulCERqxYxF7P5ulaqNjJU6nH8qzuxX/Zju1/nCpLqhVQbTviexctondrNOLFrQAXJtkdjyX4i0yovkp4GE7uUBfnxUudvfOlc5gphMPj/dt7rY/Nqod0XLBImParMmt3Y8b3iuMbsxZ5uFgBZBRsTPQerQO2UaKbl4L2QCqShawebd3qwc+3Sz3ttEJZPDP55UwnlHJWu0714adNeA+I14Jie1J7vRUZ0os69t4OLCy/i8lruREb28HpirLoWb7Zs3rreDUvGpNKcZBg9ZJLUy4XsGeFD2GXSa+vXm85KrK4NletNLNp0BwD1NvlrfWHIZoobmTmlo4mw6Q3TTLNeI+UQNxEyAXjTs3TbQypbef2kt6BquGcs+suAoPAKhkF9KgaW2U8+UGB8MGwOnWkPUFoHVKqj9DvHDwEA/9U1QT5nWKcLMRftxPEj9UTDiEVAQ10nXscW9tCN78VVyZK3qc8txzP55CGa65uDcy9q3xjSC/PYltJrJnTCMw/lvBjQh0LEzzu62xtFtd/s7tI2RSsMlL4gdpkuaJRNnrW+MLBsLl5C31T1ATbNZssW5y8o4dmBywmbZhhxSU3rOlU6H5PPxB6t6saGJ6ef0Nbh0trWtrYrtefbk6El4iTamkBjyXb6+gKLW+quklQ7yzB7KYYzBDQlyxad7gwseSaVU2lSi8ncc3RIKpYt3ZV29UHt7FWQ1Wp/ZMAo7jjJyhO/3Fkn9xrvXibSdukAs4v9LPUowURLmJ16cTYtPSQovzd+VFnJuw9MKx8pxefGkLArnzWoMi2lK1l4SHKnhmTSdgU/F90dx29oCLhfIFev7AceRgaP/DxgtRW/MDpiabXf9T44lYhinQ97uaQJPg+qysm5kgAHyOncrt6zYx4gqRCaSWrgSKMePYnXrCvTSFcK96zKg8rCMB6r2/QEqZX29U9+7lQPBiK8PTLPJDt3ysw47nBZOaBMLoVj8cPx/fmdiZ6ThmN1hw0NVXk/VtuZ60/1uvTzyoGkALB4z5ad21p3aW1rW9tzYc+9JyNdQLpygqJqPCQgWl4feTmbHkfdgYs0yYiKY4+/mdClpYsGBWNn0wOSt6AzlEt8NtSzTqrOdkiH7CcG6ybFZrbs7De4G85vx7xRcdKYB1ETyLbqrD2AHkR/DEwyM+nYpYn3ElkPi6CdKBjwTD2vXZJFZb0EdPx8tupMF7s41iTpxsg8J2pZnb+kHlsdUKnX8vHTF/ScBPu/NEz85j0Anmka6Xt11yOtYsd6L2lrSpa9jmS54BGkq8528+xUPbfpht8XF/hT6o3MOGOMw6ZMey0EnMDQIzlXb+W6JpEfepKXnD19wB09bpvH7cKAkM01p1NtiN3TJ7ZNBZM3Yo8aPRlL2obU+WmoTFmHgTQMx2otFI3PPRAhB+xwf1J7vheZLoYEq93c3cgeMhOIxEzpXB/ubcUOjBO78Vhlyk+Whmmh8captgtHdzIZt2xRT4in8Z4ohiJM/DEMmt8sLMlnHLttsN4iLgKjN85R7w9vGrq3Sd31mP+8R4a0FVY9m/hDQzfY2eQy40GevRgXttEjR0svb8Q5YoUFCeycxgfOHFgxdMo8YU33uqMG0dyTpcs9XcDBBz/g/Jb2G52zMTL+ZHFWoy1VZ0gXmey4p9NU+YPDkMWqeOekUHBiLT5c9aYvmNjVhft4ZXQbTEBzQZEmeINh7QyH9jkLjYHlnqJ5FZHLqmGXSi8UsmnG6L6SSZHhkP11bXAtLlavpFeZ6t1DHLcVA6gNNUptw3EEd2Mhv5GoJB428r7qo955jCe1dbi0trWt7UrtufZkQhqVBcXBuqbMR9e63sxsBWfpNj9tsNIS7HKPujfjt8Wn5OeNlf+qLU5Z6p3F7LmZpvYbVspUN3Tj9SUWikam9xQ9EwyOcf4V25bEppkbH4Lt1ESsxl6aXodwz9qR9OgqtOv3uDFPh+jXeiuzHY0kWzRxvilDD9ebmYV0LPumlaNv+VsN6Rcmgkw14O/NYgk5PxM0U51JdZqIg2lHhYVJvLZdBpSKq1nueZ8XFQ/Zub4gZuTcFRg4L/l5g9XOkGIhrVKU7BWjOD3Jq45WhqY12tPdzBQ4zRs+qtCO9B47HlJntiOxhCwT58kqeLKWHgq9hgDf+tXxaEdO68oes/LQ6U2IsUoVW7TaKyHV0KvN4Dgq/p29UGLyqaP4peuxEJIyj9Cte5fWtra1PSf2XHsy0mo8GjwpuXlPNYWve36lzwsDRCQnvQVqMgHREwGiGuLAQtrretYdfzuzndcATavOcjBWBmfyceR0k5YzqcVLwJUn4/gawW/Tuys/FwURZozhq844YKxHRnej0GdPFP/LHiomuNNF5/wmTBCT4Cvxz/WJwTnf9DTaXIxgijbRvM7sVmG/v13G8aefbr1Le0HOHe0rupZbPietvKxNgKB5StPUe7hWwwRtUgdT3iRlabWduWLikfd7MTlvZGLUYZp6B3qfA8i0pEnxOsnMy6OxK1san292SzcbhVOCatKZ3kizmRtxjyWuR4l5FRu/EbvYq2sTy1dRB8qR4Z7Ho2okOhjIzzTKjho015S0vqfxDngf2dOw53qRCWlsekwqx5uQsMmkSTLpNd6xgRCWIK53exy8C7YdDNv1pQ2WRCTStTxsrDHS4Os93Mb4vvIE91r6OwWhmoTpbmZoZDbb5aethWbjhyQlcvTqRat2MuSnQ5oB6T/rzYWk8JbLznqTo9+gJh1buftujYDBQ64girXRxag47+zBYhhJmH5InVM3TzV0mCTeGqF88KxopavOKTWY9O7NrS0sy9YWOybJrQ0g8cXFvrcKlky1ZtnWqTcs7B07Puoi7kragOIwXttOMTRBfINqptPBHEQ2Q8XpaONrcVLbNeW94AWAxuILTywHq/4cf802AKA8bi2BT7Mw78TF9Ijz6TLptaTow1J3RifCZlJaV6ZW+XxSW4dLa1vb2q7UnmtPJmkCRg8rNGMvVxoKk0RSpVxKYCVV59o6mpSDOMaFO6yVR3uUD0amJK4OYILnPZeZDYl9jMklAa1FdwlVKV1AULfMtJXuerlzpbuzJalXXmbNH6zsczwnw49QAO7R0nbUPpt/PRkmbU0PqOw1IdLryhMXotfzW9zIUT5iEnbY11RvZZjvx4OcV3FeVjuCjbv0IOLxmUTOz1vMbwxRyeVZa2PiPK52cgtfL1IhtOMeWRmxNJPEE+EVIQ41FjdHg3Nn0hnSS8jypeDUCvx8flKh3tS+IfWGSeUpTbDCg9OLFBYe0YMxsbeeVzm6H0P/emdk9+L0TapwpD3uZ7/XgSGnMr2oLk8tpKTHKCEZlMkBGO1FcdIYfuhJbe3JrG1ta7tSe649mZCISYwycdqaRKpK0taJgZSIpKx2ip7UaI+kh12quuIXxxRnF1/VezpNTNBNlMqxmbrErO1U2+x5Soys23tIBFk1zAf0jbvi4lbcOdNFi0xDZwNeTRMgcXQu4ACreiuz3qx0pqXvm+Nep7DmPdrgekVMWNdkzz83SkbmXzoRSzq2I47Hx290B5onKU4anN+KHkzdxde2Xm0vJWHZpdxMUvNq6EFk505ORpRqtuhsHgwIyHEEwGDdTGqfNEipJSS81g1GKl/MHEh5QICaK3Ya8fzY9+WGPUBSGC2mIXl72lp2vTkfR5WpaVw8vnR9EKYyBMwa80isJH1em1oCc2tpRc+7J4Or7yW105caUE8ESTekc7X+pyY8NRdk7cmsbW1ru1J7rj2Z2JnbIFk0xiNjvSaZr+rcvTrbAVv3eB7EfMf5+6a2s/MYda9bmZUVxrDSBttFsWBLwJl3sZIEnOCp48YJudl7U+bmwZQPY/zdTopLWtWmRZIIMu3qTpQAK1uK7VamdrjtgLP8RPMMU+opNU5XypL+Voax5nO4G5IXZ3lrYuXbdO5l9uOviBCBifLspL1+L+6Y9HzObxcG6Puy7UcAgF/dvoni3EnEAM/htKMUs1uE9sfvrXYz8z5IlZpUneUlyDUz6FLu9WYBgCyDXVOeUzvOHXp/oh6pdqIH8dwK+WnKw9qOxwrO6N7cNIrotbDcXh7WDlPQfFB2MLP7gx4KrZmkg5wKx19Ph5+vN3O7fgQkGllZkXj5m5pctcMr+uT2rrfkMjA8Rrp4OtWl53qRCamg2swh08xuKOICeOMC7vozGTt57dyStuz/yGbuYhYHmnDbo7saDCFcKuK2nmTObqYPbb07dvwDQ7SZhyS8gETrTl89N9KsZkuF4+cVsjklQy+EdLMGQXE9rSVyF+gUH2O9LBouje5VTsrVCxUZNhIbkc9aDzuXw3PK5431aEHntC0TjB8N0aPLndQIvagltNgnbgeoI6AUhysVLjtqB5QNQC9pW0ikjoCX0vvNk/z8ajfHxqcjbqTTxlhuJPUkc0KrN3UBn+aOsdHwNz+tgZR9XeVgvtNVh7rH1sf3WNo1zafdkeFdyFbH8nJI/UEmCVr1whaKA8XMbMff5PyP7y2MtsKUGLIEIz0HcjAnbbANigvn+FUlnLq54WEkQ6TGybn6/V78XPnIQ34ev/8MPYmtw6W1rW1tV2rPtSeTLGps/NO7WHz1LSs/pzOltmy1TLdqbadnmTPkqVEb0LJFOxS9B5AfcPeYYvLazL4LAOODBRrt5OXO10wzV5PUHaG8d27HZMJy+pn4Wr03choCC8eC7eiklGTStrrmZEe5elSL21MnXeKW0TtWOvckJhAT5CavSgBWKuCe5RpV+pu7Izu+l0MTS2yaimIAVltKkKRcvwyl8rkgiILVOqdwMAXEzSE9R7bo7FzY1WyhKZyYarxsUGnHupW6Ff2crFrzTAwJLYJUz6XrJWbpqY3vxusiSuRU3dw01QnOKdpg9wDvuXqrsOtOM0/waImMukv0CAOwuhHHTaAow6x6q0D5jz8d5+qrX4p/ywTJaNjxnx/MjXeYobDM4lildYVU6AAAFFxJREFUmULSC6wBqxYJ4m+UKn9bXZ8ipTdGT+o1RRTf2LAw7EntyjwZEfkhEXkgIh/vvfbfi8ivisgvicj/ISI7vfe+T0Q+JSK/JiLfclXjWtva1vZs7So9mR8G8OcA/JXeax8B8H0hhEZEfgDA9wH4YyLyQQB/AMDXAHgBwM+IyFeEED5nA0XIUrT724PXlrdizM8VudkZIz2PKzd3oGazsJ2RGsMhSyyez+9FDSImGovjlX2X3k4oU+OpaTVRmK4681byYz1uoonUR+dItob6OOm8QXbG+F6D/qZDqT0xYHlRNXGKoyW6Qj0IJnLPaovd6fEY58i08PdIZJV7HwzfS5a1CdcHUmBWPc9hwfKs7pitt3EYLGArNWKq8ti7xuPnBZtvxN/aK6N3eO92iuldgu80uUoSsFycKpUd449WXqY2oqXg5VsSbZPjZ96Y18LrmJ2sIDqn9NSSsyWyU4Xea16Hngznp29dzwNOT9TT7Ol+8V6z+yRPTb+IndEi3q1tfD8jUq4K2q98T/xfzf1k8xZJRVpRTcxOCiTq0bFUX7+4F/+5qJEumQTu6XqTalM9q+LBuV13XoP6enx+ys88Qnt9+Gy9U7tKLeyfE5H3XXjt7/T++fMAfp/+/7cD+PEQwgrAKyLyKQDfAOAffq7fkABI26K8ew6p4yRlo5w/BkBvFLq6vNnOa+T64MpCMRKbox7qUjERmozNThZAo9WIHXVzD2e+MIgy4M9W6DbYq0Q3VR/85QrCsRGjUbd2wYV9UlWN9HyI74B+Jj08B3ZUamXJB6HxBZA3EZsEQ0Bypn02GgImi87GhEqT5De2UDxQxig9z/pGPKfxr95D/d7rcRhcxOoWC8qH3OPiBUzfVCqNmT8cANCgV7UAUalApbQLxeGwByepPPwhOrUUx5TwYU1nlSfHGcIcxA2im46Rr8j7rO89PEb90n78ES5OixXaKUXsdbHRBzOdVWinynlMwqyjuS+eC11QdidIFjqnwpKd/jvxRT2ds1mxQyj1XtDFKHuk494YO75HLX9wBlnqpsW/N/fQqnRK/mbcUMNYkcXj3O6PlOPKEltE+V5X5hZ68j4Jeo+GLIWsvsjDpcewfw/A39L/fxHAa733XtfXLpmIfEhEPiYiH6ua2RUPcW1rW9uT2ruS+BWR/xJxg/tRvvQWH3tL7r8QwocBfBgAtse3gzRdFD2r6TqqZ3AYy3nprEAodHXmbnc0QzeJu5esFHfStuaZhEnPgwEgy8pKxwl3o7JAq6FLehi9gDAukWgIZTusfqbeu4b8gYZhDIPyDOC4TzTpuDGx8Sbn88Gx2r0tJLPhjoOuQ8h1tz3X8eoumnQBaNVD0l2uneR2DOQMLWrzqOjF5Q/1nDYnyI613KqenUhPu+mTr8bPfdUHrXRtomDcpY9atJvqSell3f+nC/cOZpqsXTKJHAyrQm2mkIiHs4oVajYK5PfjLp7M1WPb3bJjiIYu3ZaGgqsK+asRp9Nej+nAsDVFMtffP4vzLcvKzj0/0BL53qZdC2moe6ve82cfACMVZNvUkPgo3n+yv4d0JvZdAEiOzu0eQElmKg1rH1UIG8OwGmmCTj1YmWmJ+fDMQjLe+7wn0pMFoPeynOp13N5Eqp5us6MI8nkNYaFEk8Z480H892QMFG/f/f+F2DNfZETkOwH8XgDfHALjGLwO4D29j90BcPdZj21ta1vb07dnusiIyLcC+GMA/uUQwrz31k8B+Ksi8j8gJn5fBvD/PdZBQ0ByeAYoIQ9j4TDXXX08sp0p0V26m4wsxrX4erZAdy3ubslR3L14zDAd2zGMZPp8gUS9oKC7FkbXLSa3cuKJJm1PCwT1HAzReTpD2FIOym1Fqy0r865s99K8UVI1wOFxfI1i6DtbSGZErunYlk5yxflg8jh7uIScxTCzO42eVbK74zuqzhuP1e1v+3eP9ZJ1Ac374ri7l2OZNVt2lmjl563DPEssYVmmjc2BAdCYLNXv1fsTFA/jGA24+NoB2r3opTCnFKREmCiATv/KkrkQQfvq6/F/X35/fC10tjunh5rHmM2BnXhcy6fQGz7r5V/ePIjv7WwCvO5T9TiSxL0Uej6L6FlJmlhuLTk41vnI/Lu8X6BDnOQQvf+6m3s2LzwuvWw0jX2O1yp5GI8fFgtA1DOdKnnbwRFkHH8z13sZ8wWC3vM8Z4z1+JOxz8cT2pUtMiLyYwB+B4B9EXkdwPcjVpNKAB+ReAI/H0L4D0MInxCRnwDwzxDDqO/5fJUlAEAX4sMfgj3AXBhkP16gbnMMef0+ACCoy5scn6Pbjf8v9/TmGY/sQWhv7MbP3Y2SqpgvAb0pzKVerCy0oKscqtpf0wvPm0LmS0hLN7v194wFrXe6+iBwceENEFKxUAh3rsW/9x8hvHgjjk1DxMCE9PaGLVgMy7qtsSXikonifBLxBYrnwpDt4NTGYyGmdN4EyTBLxBLDlmjXR6ed5IZVKRJdZOoW49c09GO1SMti5WcP0O4MtcTbGzuu2jCncFlqFR4urN12XPyS0zmSr/gABrazZdU+u0+SxO8dvmcJfUG3ER/S5FhD3fnSFgjREA0ido2aN6IDnn7wK+J7dYNEE8Sd3lfSdOh+/ZV43C9/b/wcQ+ijU194lp54DUv9LS5OaYqgY7OFUO8hyTJLENuCubWJoDzE3ADD7hbkdDY4BrY27DwlvGXG4gu2q6wufcdbvPyXPsfn/wSAP3FV41nb2tb27thzjfiFACFN4oq8GpZBbWeoGshmXJ2564aygHzmjfj/L96Knw/B3GCGQVI42a8n3pTZ/tYu0kfqrnL33xgjUc/Idi3ddcNkZCXBUPpxOe4wVu2fpgUeRQZ58yp0x5JVA9yJ4zVXeXsLcqZi87djedZCgbJwHAbPLU3Q7ehuf667Yxcsedj3BADE3Y87vP5pNl3ZgX+7DEZzEYg3UWxOfu8E4cU4H7mQbjRDqmVTK99r4rq5vQuw2S/3pGmycG8MALLjpe3AHLdBErannhjVMKW7dc2uH5Pe7ebIyrc0uxZwL4ufR9eh05I3k+l9YbT0y9+v56dzUGQ2f8lh9IbC1hTpndvx/x+oJ70x1fc2rBRNDxy3rgN7OzpHcxsPPanumoZ79HzHJaBeZ9BELm5c8+Ru4t4nz4teDr24sDGxa/Cktu5dWtva1nal9nx7MkmCMB2h2Rkhf9DaawAMrZi+9gDti3GHJ2Cq2xpDxlEulSXm5HxpyU/riD5jXCvmGVmCsW49r6PeisyW5k0kmlxr99QDajtAgXp95GVC4B13l8MDhNs37P3+5xGCA6pUtKjbntj76Yn2V02GgEAAqDWHk795jG5X+30ILJyt0G5rWVNzHMxThElpncL5o7iLposaQZSyckXBdqdIKF8/1nOPY2z2N207yxLSRlRodBzZ0Xww7vRo5gndVHNhi9rOnaXxbqNAskwGr7XXVD/obAVRWEDQsjZCuFSWTU7mCCNe08aOy3Oy8+vNh+XW1EIqnphdKWWGSQEHNNe1rE5A5PkKq/fF61HcV69J8zahyJHwOt5U4GDd2FwmvP9WtQP6eG+ysLBcob4V7/9Cc3jt9sRgGOwiT87m8doAyO7rNdvXsdZt9Jyfgq09mbWtbW1Xas+1JxMSQTspkJ5W6Ca6+xjgTnfHzall6zuFYcuiNqi+Qae7gEb5Y1KDWPv0WFk28XXZsu+6k3Tbk57shFYvWD1qg+UcCLxqbnl5uCURddj3/hSWy5kHurZheQ7G7c1GjkL1jQg6IxQ+PVmi052dfTjt7tRAbYX2aIUic3kX3TE5hiCCTMmc2OfSf7+deH6pvB89h85g+jr+urNWgElCje3EchnNDvumyHbleQ7yuNR7Ewfv6fVpRiXSA/1NzTOlpzpXGyWweW342rTw3iKt3AQdKwCEhF3SBAlW5ulaO8mssnuM3f3Zg1N02kNn3ifbVprOIQsGoSh97glFYOVuufJqFz2rnV5+Se/Xblr6ONX7rF+Iea+s89/ksdqNwjryWSFFWVg7Az9nbShbz0EJ+5lYCEiqJuIIFPtRv6Cl6y3y0ibmjjMkCJPc+lToDmOUG+MZjWFIsz12KgY2vmWJSZOmRG92ve9oKOCLSGmSte1+fPDbUYqkKQaf6yaFC5TxWOrSIhEktSal1Q1OF7k1TTJJSbqL+vrEZG0p6J6drsxd7nT87TT3RUgbDI0lbru0hz89JxFTgiA6bgrSt5E6AAAyDTOZdE5XjelhvVjGpPbff98Em69oWEC1Ai2B95tV2fgIOH1C1sPhcG646K1uqQzu0RLpsdJs7DsOxjYj3XCSqrFentVNDW31Ac2bzkm/OLZUrLeJ4Xd9e8ebTYlZqvw9cvYWB67X1G7FcXABZ2AbioltgOgtLNyE2AclnVNOVHfiPZ/pYlrd2PAm2F6SvLoW78lypnK80wJBUwOGaRI/Psqnszysw6W1rW1tV2oSnhLg5t0wEXkIYAbg0bs9FgD7WI+jb+txDO15Hsd7QwjX3+kPPteLDACIyMdCCL91PY71ONbj+OIcxzpcWtva1naltl5k1ra2tV2pfSksMh9+twegth7H0NbjGNo/t+N47nMya1vb2r647UvBk1nb2tb2RWzrRWZta1vbldpzvciIyLeqTtOnROR7n9FvvkdE/q6I/IqIfEJE/oi+viciHxGRT+rf3Wc0nlRE/omI/LT++/0i8lEdx18TkeLzHeMpjGFHRP66amr9ioj89ndjPkTkj+o1+biI/JiIjJ7VfLyNzthbzoFE+7N63/6SiHz9FY/jXdU7e24XGRFJAfx5AN8G4IMAvkP1m67aGgD/aQjhqwF8I4Dv0d/9XgA/G0J4GcDP6r+fhf0RAL/S+/cPAPjTOo4jAN/1DMbwZwD83yGErwLwW3Q8z3Q+RORFAH8YwG8NIXwtgBRRy+tZzccPA/jWC6+93Rx8GyLF7MsAPgTgB694HB8B8LUhhN8M4NcRGSpxQe/sWwH8T/pcPV0LITyX/wH47QD+du/f34coHPesx/GTAH43gF8DcFtfuw3g157Bb99BvHl/F4CfRqSQegQge6s5uqIxbAF4BVpE6L3+TOcDLquzh9iT99MAvuVZzgeA9wH4+OebAwD/M4DveKvPXcU4Lrz3bwD4Uf3/wTMD4G8D+O1Pe16eW08GX4BW01WZitd9HYCPArgZQngTAPTvjWcwhP8RwH8OgF2E1wAchxBIBPIs5uQDAB4C+Msatv1FEZniGc9HCOENAH8KwKsA3gRwAuAX8Ozno29vNwfv5r37jvTOnsSe50XmsbWaruTHRTYA/O8A/pMQwumz+t3e7/9eAA9CCL/Qf/ktPnrVc5IB+HoAPxhC+Dr8/+2dXYhVVRTHf38kdZTIogQhbLTSiIKhh7CcsmgK8sF6SFCEZqCeUqLElzA0eyoI7aFUgijpQ0OTGoKkL0OUjCyujuBUWoYGpfVQEBPIsHpY6+jt5jgfzjnTZdYPLnNmn3P3WWfNnsVe++y1lseSVeUqniXWOx4AZuEVL6bibkkj/4c9G2Mydi+m3tnF0MxGZsxqNUm6BDcwb5nZzmj+VdKMOD8DOFWyGPOBRZKOA9twl+lFYJqkIka/Cp2cBE6a2Zfx+w7c6FStjw7gRzM7bWZngJ3A7VSvj3oG0kHlY7eu3tkyC9+oKjma2ch8BVwfbw8m4gtY3WXfVF7L5VXgiJmtrzvVDXTGcSe+VlMaZvaUmV1tZq34s39mZsuA3ZyrMV6FHL8AJyTNjaZ78NI2leoDd5PmSZoSf6NCjkr10cBAOugGHo63TPOAPwq3qgzq6p0tsv/WO1siaZKkWQyn3tlwKHMxruwPsBBfLT8GrK7onu34lPIQUIvPQnw95FPg+/h5RYV6uAv4II5nx0A5CmwHJlVw/zbgQOjkPeDysdAHsA7oBQ4Db+A1virRB7AVXws6g88QHhlIB7ib8nKM2x78jViZchzF116K8bq57vrVIce3wP1l6CbDCpIkKZVmdpeSJGkC0sgkSVIqaWSSJCmVNDJJkpRKGpkkSUoljcw4RFK/pFpELB+UtFLSqI8FSZ9LGnHSaknPSPo5ZO2VtKmQU9KzkjpGT9qkLJq7uFsyUvrMrA1A0nTgbeAyYO1YCiVpgpn1NzRvMLMXwrjsARYAu81sTfUSJiMhZzLjHDM7hacbWBE7UCdLek1STwQ83g0gqUvS+5J2Re6RtdHeGrOMLZGvZIekKY33kXSfpC8kfSNpe8R+Iem4pDWS9gKLLyDqRGAynq4BSa9Leqiuj3XRd4+kG6J9QcyCavEsl46e5pKhkkYmwcx+wMfCdGB5tN0MLAW2SCoKRt8KLMN3+C6uc4XmAq+Y5yv5E3isvn9JVwJPAx1mdgu+O3hl3SV/m1m7mW07j3hPSqrhu1i/M7PaAI/xW/S9CVgVbauA5TFruwPoG1wbyWiTRiYpKCJy2/Et+ZhZL/ATMCfOfWxmv5tZHx6A2B7tJ8xsXxy/WddeMA9PLLYvDEYncE3d+XcuINeGMBLTgamSlgxwXRGo+jWeTwVgH7Be0uPANDuX8iGpkDQyCZJmA/14lPD5wv8LGmNQbJD2s7fADVRbfG40s/oMdX8NJqN5ZPUu4M4BLolK9/QTa41m9hzwKNAC7C/cqKRa0siMcyRdBWwGXjIPZNuDu0RImgPMxIPnAO6V561tAR7EZwoAMyXdFsdLgb0Nt9kPzJd0XfQ7JfoejpzCUzccG8Z3rjWzHjN7HnfR0siMAWlkxictxSts4BPgIzyCGWAjMEFSD+7GdJlZMUvYi7tSNeBdMzsQ7UeATkmH8PSX/8pZa2angS5ga1yzn6H/wxdrMofxGcrGYTznE/Kk4gfx9ZgPB/tCMvpkFHYyJCR14SkJVjS0t+JpJm4aA7GSJiBnMkmSlErOZJIkKZWcySRJUippZJIkKZU0MkmSlEoamSRJSiWNTJIkpfIPLFe2kHkr+9UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot range-doppler plot as an image\n",
    "plt.imshow(range_doppler.T)\n",
    "plt.xlabel('Doppler Bins')\n",
    "plt.ylabel('Range Bins')\n",
    "plt.title('Analyzing the Range-Doppler Heatmap')\n",
    "plt.show()\n",
    "\n",
    "# Feel free to add any additional plots for futher analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Range-Doppler Plot\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you correctly analyzed the range-doppler plot, you should have seen that there are multiple objects at the same range. The only reason we are able to distinguish them is because they are moving in opposite directions from one another. Using this information in the movie example, we would could certainly have a lot more information on friend or foe and how quickly we need a decision."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extras\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benefits from mmWave Radars\n",
    "\n",
    "That last exercise may have seemed very straightforward, but there are already a lot of things within your reach. For example, I want to bring up Google Soli, a project that works with radars for gesture recognition ( https://atap.google.com/soli/ ). They use mmWave radars for their high sensitivity to movement and velocity. These properties enable small gestures using a hand or the fingers to be registered as some pattern in the data. Cameras would have a very hard time reading the minute changes from frame to frame, but mmWave radar doesn't have a problem since they are fundamentally different. After training on what specific gestures look like, Soli can classify the gestures as \"change volume\", \"swipe\", and many more. They go over some of the many benefits to doing this on their website and in their demo videos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recap\n",
    "\n",
    "This module has gone over how the radar takes advantage of the **doppler effect** to encode **velocity**. Small changes in the phase of ADC samples over multiple chirps can be used to track micro-movements and can then be converted into velocity. Applying this knowledge, we can perform a **doppler FFT** just like the range FFT to get a **range-doppler heatmap**. This heatmap is simple, but can be very powerful at determining a number of things. We showed one use of this by looking at a dopper-range plot with two objects at the same range, but opposite velocities. Range alone would not be able to help since the **two objects would have an additive affect in the range plot**, but the doppler fft gives us a **whole new dimension** to work with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking Forward\n",
    "\n",
    "So far, we have been making a simplifying assumption about the radar we are using. That is because we have assumed that the radar we are using is a simple single receiver (RX) and single transmitter (TX). We can do a lot better. We now have access to radars using **MIMO TX/RX** setups. Just as we've learned that the ADC samples and chirps give us valuable measurements, MIMO gives us a new piece of information, **angle of arrival**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "#### Contributors\n",
    "- Dash Kosaka\n",
    "\n",
    "#### Questions, Issues, etc.?\n",
    "Contact by...\n",
    "- email - presenseradar@gmail.com\n",
    "- github - https://github.com/presenseradar/openradar"
   ]
  }
 ],
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